Determination of meaning of a phrase with ambiguity resolution

ABSTRACT

A method executed by a computing device includes determining a set of identigens for a query to produce sets of identigens. A set of identigens of the sets of identigens represents one or more different meanings of a query word of the query. The method further includes interpreting, based on identigen pairing rules of a knowledge database, pairs of sequentially adjacent identigens of adjacent sets of identigens of the sets of identigens to determine a first most likely meaning interpretation of the query and produce a first query entigen group of query entigen groups. The method further includes identifying an inconsistency between at least two query entigen groups of the query entigen groups and obtaining an inconsistency clarification based on the inconsistency. The method further includes selecting one query entigen group of the query entigen groups based on the inconsistency clarification to produce a final query entigen group.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 63/094,825,entitled “DETERMINATION OF MEANING OF A PHRASE WITH AMBIGUITYRESOLUTION” filed Oct. 21, 2020, which is hereby incorporated herein byreference in its entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and moreparticularly to generating data representations of data and analyzingthe data utilizing the data representations.

Description of Related Art

It is known that data is stored in information systems, such as filescontaining text. It is often difficult to produce useful informationfrom this stored data due to many factors. The factors include thevolume of available data, accuracy of the data, and variances in howtext is interpreted to express knowledge. For example, many languagesand regional dialects utilize the same or similar words to representdifferent concepts.

Computers are known to utilize pattern recognition techniques and applystatistical reasoning to process text to express an interpretation in anattempt to overcome ambiguities inherent in words. One patternrecognition technique includes matching a word pattern of a query to aword pattern of the stored data to find an explicit textual answer.Another pattern recognition technique classifies words into majorgrammatical types such as functional words, nouns, adjectives, verbs andadverbs. Grammar based techniques then utilize these grammatical typesto study how words should be distributed within a string of words toform a properly constructed grammatical sentence where each word isforced to support a grammatical operation without necessarilyidentifying what the word is actually trying to describe.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various serversof a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devicesof a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system inaccordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collectionsmodule of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system in accordance with the presentinvention;

FIG. 5C is a schematic block diagram of an embodiment of a query moduleof a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system in accordance with thepresent invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigenentigen intelligence (IEI) module of a computing system in accordancewith the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system in accordance with the presentinvention;

FIG. 6A is a schematic block diagram of an embodiment of an elementidentification module and an interpretation module of a computing systemin accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system in accordance with the presentinvention;

FIG. 6C is a schematic block diagram of an embodiment of an answerresolution module of a computing system in accordance with the presentinvention;

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system in accordance with the presentinvention;

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things and representations of things within acomputing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within acomputing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table withina computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system in accordance with the presentinvention;

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within acomputing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system in accordance with thepresent invention;

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system in accordance with the presentinvention;

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge within a computing system in accordancewith the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of acomputing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query within a computing system in accordance withthe present invention;

FIGS. 9A-9C are schematic block diagrams of another embodiment of acomputing system illustrating a method for determining meaning of aphrase with ambiguity resolution within the computing system inaccordance with the present invention;

FIGS. 10A-10B are schematic block diagrams of another embodiment of acomputing system illustrating a method for processing informalutilization of words within the computing system in accordance with thepresent invention;

FIGS. 11A-11C are schematic block diagrams of another embodiment of acomputing system illustrating a method for identifying knowledgeassociated with a set of phrases within the computing system inaccordance with the present invention;

FIGS. 12A-12C are schematic block diagrams of another embodiment of acomputing system illustrating a method for identifying medicalconditions within the computing system in accordance with the presentinvention;

FIGS. 13A-13B are schematic block diagrams of another embodiment of acomputing system illustrating a method for identifying true meaning of asarcastic phrase within the computing system in accordance with thepresent invention; and

FIGS. 14A-14D are schematic block diagrams of another embodiment of acomputing system illustrating a method for processing a query within thecomputing system in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computingsystem 10 that includes a plurality of user devices 12-1 through 12-N, aplurality of wireless user devices 14-1 through 14-N, a plurality ofcontent sources 16-1 through 16-N, a plurality of transactional servers18-1 through 18-N, a plurality of artificial intelligence (AI) servers20-1 through 20-N, and a core network 24. The core network 24 includesat least one of the Internet, a public radio access network (RAN), andany private network. Hereafter, the computing system 10 may beinterchangeably referred to as a data network, a data communicationnetwork, a system, a communication system, and a data communicationsystem. Hereafter, the user device and the wireless user device may beinterchangeably referred to as user devices, and each of thetransactional servers and the AI servers may be interchangeably referredto as servers.

Each user device, wireless user device, transactional server, and AIserver includes a computing device that includes a computing core. Ingeneral, a computing device is any electronic device that cancommunicate data, process data, and/or store data. A further generalityof a computing device is that it includes one or more of a centralprocessing unit (CPU), a memory system, a sensor (e.g., internal orexternal), user input/output interfaces, peripheral device interfaces,communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be aportable computing device and/or a fixed computing device. A portablecomputing device may be an embedded controller, a smart sensor, a smartpill, a social networking device, a gaming device, a cell phone, a smartphone, a robot, a personal digital assistant, a digital music player, adigital video player, a laptop computer, a handheld computer, a tablet,a video game controller, an engine controller, a vehicular controller,an aircraft controller, a maritime vessel controller, and/or any otherportable device that includes a computing core. A fixed computing devicemay be security camera, a sensor device, a household appliance, amachine, a robot, an embedded controller, a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a cameracontroller, a video game console, a critical infrastructure controller,and/or any type of home or office computing equipment that includes acomputing core. An embodiment of the various servers is discussed ingreater detail with reference to FIG. 2. An embodiment of the variousdevices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source ofcontent, where the content includes one or more of data files, a datastream, a tech stream, a text file, an audio stream, an audio file, avideo stream, a video file, etc. Examples of the content sources includea weather service, a multi-language online dictionary, a fact server, abig data storage system, the Internet, social media systems, an emailserver, a news server, a schedule server, a traffic monitor, a securitycamera system, audio monitoring equipment, an information server, aservice provider, a data aggregator, and airline traffic server, ashipping and logistics server, a banking server, a financial transactionserver, etc. Alternatively, or in addition to, one or more of thevarious user devices may provide content. For example, a wireless userdevice may provide content (e.g., issued as a content message) when thewireless user device is able to capture data (e.g., text input, sensorinput, etc.).

Generally, an embodiment of this invention presents solutions where thecomputing system 10 supports the generation and utilization of knowledgeextracted from content. For example, the AI servers 20-1 through 20-Ningest content from the content sources 16-1 through 16-N by receiving,via the core network 24 content messages 28-1 through 28-N as AImessages 32-1 through 32-N, extract the knowledge from the ingestedcontent, and interact with the various user devices to utilize theextracted knowledge by facilitating the issuing, via the core network24, user messages 22-1 through 22-N to the user devices 12-1 through12-N and wireless signals 26-1 through 26-N to the wireless user devices14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g.,requesting content related to a topic, content type, content timing, oneor more domains, etc.) or a content response, where the content responseincludes real-time or static content such as one or more of dictionaryinformation, facts, non-facts, weather information, sensor data, newsinformation, blog information, social media content, user daily activityschedules, traffic conditions, community event schedules, schoolschedules, user schedules airline records, shipping records, logisticsrecords, banking records, census information, global financial historyinformation, etc. Each AI message 32-1 through 32-N includes one or moreof content messages, user messages (e.g., a query request, a queryresponse that includes an answer to a query request), and transactionmessages (e.g., transaction information, requests and responses relatedto transactions). Each user message 22-1 through 22-N includes one ormore of a query request, a query response, a trigger request, a triggerresponse, a content collection, control information, softwareinformation, configuration information, security information, routinginformation, addressing information, presence information, analyticsinformation, protocol information, all types of media, sensor data,statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, eachof the wireless user devices 14-1 through 14-N encodes/decodes dataand/or information messages (e.g., user messages such as user messages22-1 through 22-N) in accordance with one or more wireless standards forlocal wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or forwide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite,point-to-point, etc.) to produce wireless signals 26-1 through 26-N.Having encoded/decoded the data and/or information messages, thewireless user devices 14-1 through 14-N and/receive the wireless signalsto/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, thetransactional servers 18-1 through 18-N communicate, via the corenetwork 24, transaction messages 30-1 through 30-N as further AImessages 32-1 through 32-N to facilitate ingesting of transactional typecontent (e.g., real-time crypto currency transaction information) and tofacilitate handling of utilization of the knowledge by one or more ofthe transactional servers (e.g., for a transactional function) inaddition to the utilization of the knowledge by the various userdevices. Each transaction message 30-1 through 30-N includes one or moreof a query request, a query response, a trigger request, a triggerresponse, a content message, and transactional information, where thetransactional information may include one or more of consumer purchasinghistory, crypto currency ledgers, stock market trade information, otherinvestment transaction information, etc.

In another specific example of operation of the generation andutilization of knowledge extracted from the content, the user device12-1 issues a user message 22-1 to the AI server 20-1, where the usermessage 22-1 includes a query request and where the query requestincludes a question related to a first domain of knowledge. The issuingincludes generating the user message 22-1 based on the query request(e.g., the question), selecting the AI server 20-1 based on the firstdomain of knowledge, and sending, via the core network 24, the usermessage 22-1 as a further AI message 32-1 to the AI server 20-1. Havingreceived the AI message 32-1, the AI server 20-1 analyzes the questionwithin the first domain, generates further knowledge, generates apreliminary answer, generates a quality level indicator of thepreliminary answer, and determines to gather further content when thequality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via thecore network 24, a still further AI message 32-1 as a further contentmessage 28-1 to the content source 16-1, where the content message 28-1includes a content request for more content associated with the firstdomain of knowledge and in particular the question. Alternatively, or inaddition to, the AI server 20-1 issues the content request to another AIserver to facilitate a response within a domain associated with theother AI server. Further alternatively, or in addition to, the AI server20-1 issues the content request to one or more of the various userdevices to facilitate a response from a subject matter expert.

Having received the content message 28-1, the contents or 16-1 issues,via the core network 24, a still further content message 28-1 to the AIserver 20-1 as a yet further AI message 32-1, where the still furthercontent message 28-1 includes requested content. The AI server 20-1processes the received content to generate further knowledge. Havinggenerated the further knowledge, the AI server 20-1 re-analyzes thequestion, generates still further knowledge, generates anotherpreliminary answer, generates another quality level indicator of theother preliminary answer, and determines to issue a query response tothe user device 12-1 when the quality level indicator is above theminimum quality threshold level. When issuing the query response, the AIserver 20-1 generates an AI message 32-1 that includes another usermessage 22-1, where the other user message 22-1 includes the otherpreliminary answer as a query response including the answer to thequestion. Having generated the AI message 32-1, the AI server 20-1sends, via the core network 24, the AI message 32-1 as the user message22-1 to the user device 12-1 thus providing the answer to the originalquestion of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers20-1 through 20-N and the transactional servers 18-1 through 18-N of thecomputing system 10 of FIG. 1. The servers include a computing core 52,one or more visual output devices 74 (e.g., video graphics display,touchscreen, LED, etc.), one or more user input devices 76 (e.g.,keypad, keyboard, touchscreen, voice to text, a push button, amicrophone, a card reader, a door position switch, a biometric inputdevice, etc.), one or more audio output devices 78 (e.g., speaker(s),headphone jack, a motor, etc.), and one or more visual input devices 80(e.g., a still image camera, a video camera, photocell, etc.).

The servers further include one or more universal serial bus (USB)devices (USB devices 1-U), one or more peripheral devices (e.g.,peripheral devices 1-P), one or more memory devices (e.g., one or moreflash memory devices 92, one or more hard drive (HD) memories 94, andone or more solid state (SS) memory devices 96, and/or cloud memory 98).The servers further include one or more wireless location modems 84(e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, timedifference of arrival, signal strength, dedicated wireless location,etc.), one or more wireless communication modems 86-1 through 86-N(e.g., a cellular network transceiver, a wireless data networktransceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHztransceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telcointerface 102 (e.g., to interface to a public switched telephonenetwork), and a wired local area network (LAN) 88 (e.g., optical,electrical), and a wired wide area network (WAN) 90 (e.g., optical,electrical).

The computing core 52 includes a video graphics module 54, one or moreprocessing modules 50-1 through 50-N (e.g., which may include one ormore secure co-processors), a memory controller 56 and one or more mainmemories 58-1 through 58-N (e.g., RAM serving as local memory). Thecomputing core 52 further includes one or more input/output (I/O) deviceinterfaces 62, an input/output (I/O) controller 60, a peripheralinterface 64, one or more USB interfaces 66, one or more networkinterfaces 72, one or more memory interfaces 70, and/or one or moreperipheral device interfaces 68.

The processing modules may be a single processing device or a pluralityof processing devices where the processing device may further bereferred to as one or more of a “processing circuit”, a “processor”,and/or a “processing unit”. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions.

The processing module, module, processing circuit, and/or processingunit may be, or further include, memory and/or an integrated memoryelement, which may be a single memory device, a plurality of memorydevices, and/or embedded circuitry of another processing module, module,processing circuit, and/or processing unit. Such a memory device may bea read-only memory, random access memory, volatile memory, non-volatilememory, static memory, dynamic memory, flash memory, cache memory,and/or any device that stores digital information. Note that if theprocessing module, module, processing circuit, and/or processing unitincludes more than one processing device, the processing devices may becentrally located (e.g., directly coupled together via a wired and/orwireless bus structure) or may be distributedly located (e.g., cloudcomputing via indirect coupling via a local area network and/or a widearea network).

Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination ofhardware (e.g., connectors, wiring, etc.) and may further includeoperational instructions stored on memory (e.g., driver software) thatare executed by one or more of the processing modules 50-1 through 50-Nand/or a processing circuit within the interface. Each of the interfacescouples to one or more components of the servers. For example, one ofthe IO device interfaces 62 couples to an audio output device 78. Asanother example, one of the memory interfaces 70 couples to flash memory92 and another one of the memory interfaces 70 couples to cloud memory98 (e.g., an on-line storage system and/or on-line backup system). Inother embodiments, the servers may include more or less devices andmodules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the variousdevices of the computing system 10 of FIG. 1, including the user devices12-1 through 12-N and the wireless user devices 14-1 through 14-N. Thevarious devices include the visual output device 74 of FIG. 2, the userinput device 76 of FIG. 2, the audio output device 78 of FIG. 2, thevisual input device 80 of FIG. 2, and one or more sensors 82.

The sensor may be implemented internally and/or externally to thedevice. Example sensors includes a still camera, a video camera, servomotors associated with a camera, a position detector, a smoke detector,a gas detector, a motion sensor, an accelerometer, velocity detector, acompass, a gyro, a temperature sensor, a pressure sensor, an altitudesensor, a humidity detector, a moisture detector, an imaging sensor, anda biometric sensor. Further examples of the sensor include an infraredsensor, an audio sensor, an ultrasonic sensor, a proximity detector, amagnetic field detector, a biomaterial detector, a radiation detector, aweight detector, a density detector, a chemical analysis detector, afluid flow volume sensor, a DNA reader, a wind speed sensor, a winddirection sensor, and an object detection sensor.

Further examples of the sensor include an object identifier sensor, amotion recognition detector, a battery level detector, a roomtemperature sensor, a sound detector, a smoke detector, an intrusiondetector, a motion detector, a door position sensor, a window positionsensor, and a sunlight detector. Still further sensor examples includemedical category sensors including: a pulse rate monitor, a heart rhythmmonitor, a breathing detector, a blood pressure monitor, a blood glucoselevel detector, blood type, an electrocardiogram sensor, a body massdetector, an imaging sensor, a microphone, body temperature, etc.

The various devices further include the computing core 52 of FIG. 2, theone or more universal serial bus (USB) devices (USB devices 1-U) of FIG.2, the one or more peripheral devices (e.g., peripheral devices 1-P) ofFIG. 2, and the one or more memories of FIG. 2 (e.g., flash memories 92,HD memories 94, SS memories 96, and/or cloud memories 98). The variousdevices further include the one or more wireless location modems 84 ofFIG. 2, the one or more wireless communication modems 86-1 through 86-Nof FIG. 2, the telco interface 102 of FIG. 2, the wired local areanetwork (LAN) 88 of FIG. 2, and the wired wide area network (WAN) 90 ofFIG. 2. In other embodiments, the various devices may include more orless internal devices and modules than shown in this example embodimentof the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of acomputing system that includes one or more of the user device 12-1 ofFIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includesthe processing module 50-1 (e.g., associated with the servers) of FIG.2, where the processing module 50-1 includes a collections module 120,an identigen entigen intelligence (IEI) module 122, and a query module124. Alternatively, the collections module 120, the IEI module 122, andthe query module 124 may be implemented by the processing module 50-1(e.g., associated with the various user devices) of FIG. 3. Thecomputing system functions to interpret content to produce a response toa query.

FIG. 4A illustrates an example of the interpreting of the content toproduce the response to the query where the collections module 120interprets (e.g., based on an interpretation approach such as rules) atleast one of a collections request 132 from the query module 124 and acollections request within collections information 130 from the IEImodule 122 to produce content request information (e.g., potentialsources, content descriptors of desired content). Alternatively, or inaddition to, the collections module 120 may facilitate gathering furthercontent based on a plurality of collection requests from a plurality ofdevices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection ofcontent, where the content may be received in a real-time fashion onceor at desired intervals, or in a static fashion from previous discretetime frames. For instance, the query module 124 issues the collectionsrequest 132 to facilitate collection of content as a background activityto support a long-term query (e.g., how many domestic airline flightsover the next seven days include travelers between the age of 18 and 35years old). The collections request 132 may include one or more of arequester identifier (ID), a content type (e.g., language, dialect,media type, topic, etc.), a content source indicator, securitycredentials (e.g., an authorization level, a password, a user ID,parameters utilized for encryption, etc.), a desired content qualitylevel, trigger information (e.g., parameters under which to collectcontent based on a pre-event, an event (i.e., content quality levelreaches a threshold to cause the trigger, trueness), or a timeframe), adesired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module120 selects a source of content based on the content requestinformation. The selecting includes one or more of identifying one ormore potential sources based on the content request information,selecting the source of content from the potential sources utilizing aselection approach (e.g., favorable history, a favorable security level,favorable accessibility, favorable cost, favorable performance, etc.).For example, the collections module 120 selects the content source 16-1when the content source 16-1 is known to provide a favorable contentquality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issuesa content request 126 to the selected source of content. The issuingincludes generating the content request 126 based on the content requestinformation for the selected source of content and sending the contentrequest 126 to the selected source of content. The content request 126may include one or more of a content type indicator, a requester ID,security credentials for content access, and any other informationassociated with the collections request 132. For example, thecollections module 120 sends the content request 126, via the corenetwork 24 of FIG. 1, to the content source 16-1. Alternatively, or inaddition to, the collections module 120 may send a similar contentrequest 126 to one or more of the user device 12-1, the wireless userdevice 14-1, and the transactional server 18-1 to facilitate collectingof further content.

In response to the content request 126, the collections module 120receives one or more content responses 128. The content response 128includes one or more of content associated with the content source, acontent source identifier, security credential processing information,and any other information pertaining to the desired content. Havingreceived the content response 128, the collections module 120 interpretsthe received content response 128 to produce collections information130, where the collections information 130 further includes acollections response from the collections module 120 to the IEI module122.

The collections response includes one or more of transformed content(e.g., completed sentences and paragraphs), timing informationassociated with the content, a content source ID, and a content qualitylevel. Having generated the collections response of the collectionsinformation 130, the collections module 120 sends the collectionsinformation 130 to the IEI module 122. Having received the collectionsinformation 130 from the collections module 120, the IEI module 122interprets the further content of the content response to generatefurther knowledge, where the further knowledge is stored in a memoryassociated with the IEI module 122 to facilitate subsequent answering ofquestions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of thecontent to produce the response to the query where, the query module 124interprets a received query request 136 from a requester to produce aninterpretation of the query request. For example, the query module 124receives the query request 136 from the user device 12-2, and/or fromone or more of the wireless user device 14-2 and the transactionalserver 18-2. The query request 136 includes one or more of an identifier(ID) associated with the request (e.g., requester ID, ID of an entity tosend a response to), a question, question constraints (e.g., within atimeframe, within a geographic area, within a domain of knowledge,etc.), and content associated with the question (e.g., which may beanalyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whetherto issue a request to the IEI module 122 (e.g., a question, perhaps withcontent) and/or to issue a request to the collections module 120 (e.g.,for further background content). For example, the query module 124produces the interpretation of the query request to indicate to send therequest directly to the IEI module 122 when the question is associatedwith a simple non-time varying function answer (e.g., question: “howmany hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues atleast one of an IEI request as query information 138 to the IEI module122 (e.g., when receiving a simple new query request) and a collectionsrequest 132 to the collections module 120 (e.g., based on two or morequery requests 136 requiring more substantive content gathering). TheIEI request of the query information 138 includes one or more of anidentifier (ID) of the query module 124, an ID of the requester (e.g.,the user device 12-2), a question (e.g., with regards to content foranalysis, with regards to knowledge minded by the AI server from generalcontent), one or more constraints (e.g., assumptions, restrictions,etc.) associated with the question, content for analysis of thequestion, and timing information (e.g., a date range for relevance ofthe question).

Having received the query information 138 that includes the IEI requestfrom the query module 124, the IEI module 122 determines whether asatisfactory response can be generated based on currently availableknowledge, including that of the query request 136. The determiningincludes indicating that the satisfactory response cannot be generatedwhen an estimated quality level of an answer falls below a minimumquality threshold level. When the satisfactory response cannot begenerated, the IEI module 122 facilitates collecting more content. Thefacilitating includes issuing a collections request to the collectionsmodule 120 of the AI server 20-1 and/or to another server or userdevice, and interpreting a subsequent collections response 134 ofcollections information 130 that includes further content to producefurther knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can begenerated, the IEI module 122 issues an IEI response as queryinformation 138 to the query module 124. The IEI response includes oneor more of one or more answers, timing relevance of the one or moreanswers, an estimated quality level of each answer, and one or moreassumptions associated with the answer. The issuing includes generatingthe IEI response based on the collections response 134 of thecollections information 130 and the IEI request, and sending the IEIresponse as the query information 138 to the query module 124.Alternatively, or in addition to, at least some of the further contentcollected by the collections module 120 is utilized to generate acollections response 134 issued by the collections module 120 to thequery module 124. The collections response 134 includes one or more offurther content, a content availability indicator (e.g., when, where,required credentials, etc.), a content freshness indicator (e.g.,timestamps, predicted time availability), content source identifiers,and a content quality level.

Having received the query information 138 from the IEI module 122, thequery module 124 issues a query response 140 to the requester based onthe IEI response and/or the collections response 134 directly from thecollections module 120, where the collection module 120 generates thecollections response 134 based on collected content and the collectionsrequest 132. The query response 140 includes one or more of an answer,answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpretingcontent to produce a response to a query within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-3,4A-4B, and also FIG. 4C. The method includes step 150 where acollections module of a processing module of one or more computingdevices (e.g., of one or more servers) interprets a collections requestto produce content request information. The interpreting may include oneor more of identifying a desired content source, identifying a contenttype, identifying a content domain, and identifying content timingrequirements.

The method continues at step 152 where the collections module selects asource of content based on the content request information. For example,the collections module identifies one or more potential sources based onthe content request information and selects the source of content fromthe potential sources utilizing a selection approach (e.g., based on oneor more of favorable history, a favorable security level, favorableaccessibility, favorable cost, favorable performance, etc.). The methodcontinues at step 154 where the collections module issues a contentrequest to the selected source of content. The issuing includesgenerating a content request based on the content request informationfor the selected source of content and sending the content request tothe selected source of content.

The method continues at step 156 where the collections module issuescollections information to an identigen entigen intelligence (IEI)module based on a received content response, where the IEI moduleextracts further knowledge from newly obtained content from the one ormore received content responses. For example, the collections modulegenerates the collections information based on newly obtained contentfrom the one or more received content responses of the selected sourceof content.

The method continues at step 158 where a query module interprets areceived query request from a requester to produce an interpretation ofthe query request. The interpreting may include determining whether toissue a request to the IEI module (e.g., a question) or to issue arequest to the collections module to gather further background content.The method continues at step 160 where the query module issues a furthercollections request. For example, when receiving a new query request,the query module generates a request for the IEI module. As anotherexample, when receiving a plurality of query requests for similarquestions, the query module generates a request for the collectionsmodule to gather further background content.

The method continues at step 162 where the IEI module determines whethera satisfactory query response can be generated when receiving therequest from the query module. For example, the IEI module indicatesthat the satisfactory query response cannot be generated when anestimated quality level of an answer is below a minimum answer qualitythreshold level. The method branches to step 166 when the IEI moduledetermines that the satisfactory query response can be generated. Themethod continues to step 164 when the IEI module determines that thesatisfactory query response cannot be generated. When the satisfactoryquery response cannot be generated, the method continues at step 164where the IEI module facilitates collecting more content. The methodloops back to step 150.

When the satisfactory query response can be generated, the methodcontinues at step 166 where the IEI module issues an IEI response to thequery module. The issuing includes generating the IEI response based onthe collections response and the IEI request, and sending the IEIresponse to the query module. The method continues at step 168 where thequery module issues a query response to the requester. For example, thequery module generates the query response based on the IEI responseand/or a collections response from the collections module and sends thequery response to the requester, where the collections module generatesthe collections response based on collected content and the collectionsrequest.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5A is a schematic block diagram of an embodiment of the collectionsmodule 120 of FIG. 4A that includes a content acquisition module 180, acontent selection module 182, a source selection module 184, a contentsecurity module 186, an acquisition timing module 188, a contenttransformation module 190, and a content quality module 192. Generally,an embodiment of this invention presents solutions where the collectionsmodule 120 supports collecting content.

In an example of operation of the collecting of the content, the contentacquisition module 180 receives a collections request 132 from arequester. The content acquisition module 180 obtains content selectioninformation 194 based on the collections request 132. The contentselection information 194 includes one or more of content requirements,a desired content type indicator, a desired content source identifier, acontent type indicator, a candidate source identifier (ID), and acontent profile (e.g., a template of typical parameters of the content).For example, the content acquisition module 180 receives the contentselection information 194 from the content selection module 182, wherethe content selection module 182 generates the content selectioninformation 194 based on a content selection information request fromthe content acquisition module 180 and where the content acquisitionmodule 180 generates the content selection information request based onthe collections request 132.

The content acquisition module 180 obtains source selection information196 based on the collections request 132. The source selectioninformation 196 includes one or more of candidate source identifiers, acontent profile, selected sources, source priority levels, andrecommended source access timing. For example, the content acquisitionmodule 180 receives the source selection information 196 from the sourceselection module 184, where the source selection module 184 generatesthe source selection information 196 based on a source selectioninformation request from the content acquisition module 180 and wherethe content acquisition module 180 generates the source selectioninformation request based on the collections request 132.

The content acquisition module 180 obtains acquisition timinginformation 200 based on the collections request 132. The acquisitiontiming information 200 includes one or more of recommended source accesstiming, confirmed source access timing, source access testing results,estimated velocity of content update's, content precious, timestamps,predicted time availability, required content acquisition triggers,content acquisition trigger detection indicators, and a duplicativeindicator with a pending content request. For example, the contentacquisition module 180 receives the acquisition timing information 200from the acquisition timing module 188, where the acquisition timingmodule 188 generates the acquisition timing information 200 based on anacquisition timing information request from the content acquisitionmodule 180 and where the content acquisition module 180 generates theacquisition timing information request based on the collections request132.

Having obtained the content selection information 194, the sourceselection information 196, and the acquisition timing information 200,the content acquisition module 180 issues a content request 126 to acontent source utilizing security information 198 from the contentsecurity module 186, where the content acquisition module 180 generatesthe content request 126 in accordance with the content selectioninformation 194, the source selection information 196, and theacquisition timing information 200. The security information 198includes one or more of source priority requirements, requester securityinformation, available security procedures, and security credentials fortrust and/or encryption. For example, the content acquisition module 180generates the content request 126 to request a particular content typein accordance with the content selection information 194 and to includesecurity parameters of the security information 198, initiates sendingof the content request 126 in accordance with the acquisition timinginformation 200, and sends the content request 126 to a particulartargeted content source in accordance with the source selectioninformation 196.

In response to receiving a content response 128, the content acquisitionmodule 180 determines the quality level of received content extractedfrom the content response 128. For example, the content acquisitionmodule 180 receives content quality information 204 from the contentquality module 192, where the content quality module 192 generates thequality level of the received content based on receiving a contentquality request from the content acquisition module 180 and where thecontent acquisition module 180 generates the content quality requestbased on content extracted from the content response 128. The contentquality information includes one or more of a content reliabilitythreshold range, a content accuracy threshold range, a desired contentquality level, a predicted content quality level, and a predicted levelof trust.

When the quality level is below a minimum desired quality thresholdlevel, the content acquisition module 180 facilitates acquisition offurther content. The facilitating includes issuing another contentrequest 126 to a same content source and/or to another content source toreceive and interpret further received content. When the quality levelis above the minimum desired quality threshold level, the contentacquisition module 180 issues a collections response 134 to therequester. The issuing includes processing the content in accordancewith a transformation approach to produce transformed content,generating the collections response 134 to include the transformedcontent, and sending the collections response 134 to the requester. Theprocessing of the content to produce the transformed content includesreceiving content transformation information 202 from the contenttransformation module 190, where the content transformation module 190transforms the content in accordance with the transformation approach toproduce the transformed content. The content transformation informationincludes a desired format, available formats, recommended formatting,the received content, transformation instructions, and the transformedcontent.

FIG. 5B is a logic diagram of an embodiment of a method for obtainingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The methodincludes step 210 where a processing module of one or more processingmodules of one or more computing devices of the computing systemreceives a collections request from the requester. The method continuesat step 212 where the processing module determines content selectioninformation. The determining includes interpreting the collectionsrequest to identify requirements of the content.

The method continues at step 214 where the processing module determinessource selection information. The determining includes interpreting thecollections request to identify and select one or more sources for thecontent to be collected. The method continues at step 216 where theprocessing module determines acquisition timing information. Thedetermining includes interpreting the collections request to identifytiming requirements for the acquisition of the content from the one ormore sources. The method continues at step 218 where the processingmodule issues a content request utilizing security information and inaccordance with one or more of the content selection information, thesource selection information, and the acquisition timing information.For example, the processing module issues the content request to the oneor more sources for the content in accordance with the contentrequirements, where the sending of the request is in accordance with theacquisition timing information.

The method continues at step 220 where the processing module determinesa content quality level for received content area the determiningincludes receiving the content from the one or more sources, obtainingcontent quality information for the received content based on a qualityanalysis of the received content. The method branches to step 224 whenthe content quality level is favorable and the method continues to step222 when the quality level is unfavorable. For example, the processingmodule determines that the content quality level is favorable when thecontent quality level is equal to or above a minimum quality thresholdlevel and determines that the content quality level is unfavorable whenthe content quality level is less than the minimum quality thresholdlevel.

When the content quality level is unfavorable, the method continues atstep 222 where the processing module facilitates acquisition and furthercontent. For example, the processing module issues further contentrequests and receives further content for analysis. When the contentquality level is favorable, the method continues at step 224 where theprocessing module issues a collections response to the requester. Theissuing includes generating the collections response and sending thecollections response to the requester. The generating of the collectionsresponse may include transforming the received content into transformedcontent in accordance with a transformation approach (e.g.,reformatting, interpreting absolute meaning and translating into anotherlanguage in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5C is a schematic block diagram of an embodiment of the querymodule 124 of FIG. 4A that includes an answer acquisition module 230, acontent requirements module 232 a source requirements module 234, acontent security module 236, an answer timing module 238, an answertransformation module 240, and an answer quality module 242. Generally,an embodiment of this invention presents solutions where the querymodule 124 supports responding to a query.

In an example of operation of the responding to the query, the answeracquisition module 230 receives a query request 136 from a requester.The answer acquisition module 230 obtains content requirementsinformation 248 based on the query request 136. The content requirementsinformation 248 includes one or more of content parameters, a desiredcontent type, a desired content source if any, a content type if any,candidate source identifiers, a content profile, and a question of thequery request 136. For example, the answer acquisition module 230receives the content requirements information 248 from the contentrequirements module 232, where the content requirements module 232generates the content requirements information 248 based on a contentrequirements information request from the answer acquisition module 230and where the answer acquisition module 230 generates the contentrequirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirementsinformation 250 based on the query request 136. The source requirementsinformation 250 includes one or more of candidate source identifiers, acontent profile, a desired source parameter, recommended sourceparameters, source priority levels, and recommended source accesstiming. For example, the answer acquisition module 230 receives thesource requirements information 250 from the source requirements module234, where the source requirements module 234 generates the sourcerequirements information 250 based on a source requirements informationrequest from the answer acquisition module 230 and where the answeracquisition module 230 generates the source requirements informationrequest based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254based on the query request 136. The answer timing information 254includes one or more of requested answer timing, confirmed answertiming, source access testing results, estimated velocity of contentupdates, content freshness, timestamps, predicted time available,requested content acquisition trigger, and a content acquisition triggerdetected indicator. For example, the answer acquisition module 230receives the answer timing information 254 from the answer timing module238, where the answer timing module 238 generates the answer timinginformation 254 based on an answer timing information request from theanswer acquisition module 230 and where the answer acquisition module230 generates the answer timing information request based on the queryrequest 136.

Having obtained the content requirements information 248, the sourcerequirements information 250, and the answer timing information 254, theanswer acquisition module 230 determines whether to issue an ID request244 and/or a collections request 132 based on one or more of the contentrequirements information 248, the source requirements information 250,and the answer timing information 254. For example, the answeracquisition module 230 selects the ID request 244 when an immediateanswer to a simple query request 136 is required and is expected to havea favorable quality level. As another example, the answer acquisitionmodule 230 selects the collections request 132 when a longer-term answeris required as indicated by the answer timing information to beforeand/or when the query request 136 has an unfavorable quality level.

When issuing the ID request 244, the answer acquisition module 230generates the ID request 244 in accordance with security information 252received from the content security module 236 and based on one or moreof the content requirements information 248, the source requirementsinformation 250, and the answer timing information 254. Having generatedthe IEI request 244, the answer acquisition module 230 sends the IEIrequest 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module230 generates the collections request 132 in accordance with thesecurity information 252 received from the content security module 236and based on one or more of the content requirements information 248,the source requirements information 250, and the answer timinginformation 254. Having generated the collections request 132, theanswer acquisition module 230 sends the collections request 132 to atleast one collections module. Alternatively, the answer acquisitionmodule 230 facilitate sending of the collections request 132 to one ormore various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of areceived answer extracted from a collections response 134 and/or an IEIresponse 246. For example, the answer acquisition module 230 extractsthe quality level of the received answer from answer quality information258 received from the answer quality module 242 in response to an answerquality request from the answer acquisition module 230. When the qualitylevel is unfavorable, the answer acquisition module 230 facilitatesobtaining a further answer. The facilitation includes issuing at leastone of a further IEI request 244 and a further collections request 132to generate a further answer for further quality testing. When thequality level is favorable, the answer acquisition module 230 issues aquery response 140 to the requester. The issuing includes generating thequery response 140 based on answer transformation information 256received from the answer transformation module 240, where the answertransformation module 240 generates the answer transformationinformation 256 to include a transformed answer based on receiving theanswer from the answer acquisition module 230. The answer transformationinformation 250 6A further include the question, a desired format of theanswer, available formats, recommended formatting, received IEIresponses, transformation instructions, and transformed IEI responsesinto an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing aresponse to a query within a computing system. In particular, a methodis presented for use in conjunction with one or more functions andfeatures described in conjunction with FIGS. 1-3, 4A-4C, 5C, and alsoFIG. 5D. The method includes step 270 where a processing module of oneor more processing modules of one or more computing devices of thecomputing system receives a query request (e.g., a question) from arequester. The method continues at step 272 where the processing moduledetermines content requirements information. The determining includesinterpreting the query request to produce the content requirements. Themethod continues at step 274 where the processing module determinessource requirements information. The determining includes interpretingthe query request to produce the source requirements. The methodcontinues at step 276 where the processing module determines answertiming information. The determining includes interpreting the queryrequest to produce the answer timing information.

The method continues at step 278 the processing module determineswhether to issue an IEI request and/or a collections request. Forexample, the determining includes selecting the IEI request when theanswer timing information indicates that a simple one-time answer isappropriate. As another example, the processing module selects thecollections request when the answer timing information indicates thatthe answer is associated with a series of events over an event timeframe.

When issuing the IEI request, the method continues at step 280 where theprocessing module issues the IEI request to an IEI module. The issuingincludes generating the IEI request in accordance with securityinformation and based on one or more of the content requirementsinformation, the source requirements information, and the answer timinginformation.

When issuing the collections request, the method continues at step 282where the processing module issues the collections request to acollections module. The issuing includes generating the collectionsrequest in accordance with the security information and based on one ormore of the content requirements information, the source requirementsinformation, and the answer timing information. Alternatively, theprocessing module issues both the IEI request and the collectionsrequest when a satisfactory partial answer may be provided based on acorresponding IEI response and a further more generalized and specificanswer may be provided based on a corresponding collections response andassociated further IEI response.

The method continues at step 284 where the processing module determinesa quality level of a received answer. The determining includesextracting the answer from the collections response and/or the IEIresponse and interpreting the answer in accordance with one or more ofthe content requirements information, the source requirementsinformation, the answer timing information, and the query request toproduce the quality level. The method branches to step 288 when thequality level is favorable and the method continues to step 286 when thequality level is unfavorable. For example, the processing moduleindicates that the quality level is favorable when the quality level isequal to or greater than a minimum answer quality threshold level. Asanother example, the processing module indicates that the quality levelis unfavorable when the quality level is less than the minimum answerquality threshold level.

When the quality level is unfavorable, the method continues at step 286where the processing module obtains a further answer. The obtainingincludes at least one of issuing a further IEI request and a furthercollections request to facilitate obtaining of a further answer forfurther answer quality level testing as the method loops back to step270. When the quality level is favorable, the method continues at step288 where the processing module issues a query response to therequester. The issuing includes transforming the answer into atransformed answer in accordance with an answer transformation approach(e.g., formatting, further interpretations of the virtual question inlight of the answer and further knowledge) and sending the transformedanswer to the requester as the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 5E is a schematic block diagram of an embodiment of the identigenentigen intelligence (IEI) module 122 of FIG. 4A that includes a contentingestion module 300, an element identification module 302, andinterpretation module 304, and answer resolution module 306, and an IEIcontrol module 308. Generally, an embodiment of this invention presentssolutions where the IEI module 122 supports interpreting content toproduce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of theknowledge, the content ingestion module 300 generates formatted content314 based on question content 312 and/or source content 310, where theIEI module 122 receives an IEI request 244 that includes the questioncontent 312 and the IEI module 122 receives a collections response 134that includes the source content 310. The source content 310 includescontent from a source extracted from the collections response 134. Thequestion content 312 includes content extracted from the IEI request 244(e.g., content paired with a question). The content ingestion module 300generates the formatted content 314 in accordance with a formattingapproach (e.g., creating proper sentences from words of the content).The formatted content 314 includes modified content that is compatiblewith subsequent element identification (e.g., complete sentences,combinations of words and interpreted sounds and/or inflection cues withtemporal associations of words).

The element identification module 302 processes the formatted content314 based on element rules 318 and an element list 332 to produceidentified element information 340. Rules 316 includes the element rules318 (e.g., match, partial match, language translation, etc.). Lists 330includes the element list 332 (e.g., element ID, element context ID,element usage ID, words, characters, symbols etc.). The IEI controlmodule 308 may provide the rules 316 and the lists 330 by accessingstored data 360 from a memory associated with the IEI module 122.Generally, an embodiment of this invention presents solutions where thestored data 360 may further include one or more of a descriptivedictionary, categories, representations of element sets, element list,sequence data, pending questions, pending request, recognized elements,unrecognized elements, errors, etc.

The identified element information 340 includes one or more ofidentifiers of elements identified in the formatted content 314, mayinclude ordering and/or sequencing and grouping information. Forexample, the element identification module 302 compares elements of theformatted content 314 to known elements of the element list 332 toproduce identifiers of the known elements as the identified elementinformation 340 in accordance with the element rules 318. Alternatively,the element identification module 302 outputs un-identified elementinformation 342 to the IEI control module 308, where the un-identifiedelement information 342 includes temporary identifiers for elements notidentifiable from the formatted content 314 when compared to the elementlist 332.

The interpretation module 304 processes the identified elementinformation 340 in accordance with interpretation rules 320 (e.g.,potentially valid permutations of various combinations of identifiedelements), question information 346 (e.g., a question extracted from theIEI request 244 which may be paired with content associated with thequestion), and a groupings list 334 (e.g., representations of associatedgroups of representations of things, a set of element identifiers, validelement usage IDs in accordance with similar, an element context,permutations of sets of identifiers for possible interpretations of asentence or other) to produce interpreted information 344. Theinterpreted information 344 includes potentially valid interpretationsof combinations of identified elements. Generally, an embodiment of thisinvention presents solutions where the interpretation module 304supports producing the interpreted information 344 by consideringpermutations of the identified element information 340 in accordancewith the interpretation rules 320 and the groupings list 334.

The answer resolution module 306 processes the interpreted information344 based on answer rules 322 (e.g., guidance to extract a desiredanswer), the question information 346, and inferred question information352 (e.g., posed by the IEI control module or analysis of generalcollections of content or refinement of a stated question from arequest) to produce preliminary answers 354 and an answer quality level356. The answer generally lies in the interpreted information 344 asboth new content received and knowledge based on groupings list 334generated based on previously received content. The preliminary answers354 includes an answer to a stated or inferred question that subjectfurther refinement. The answer quality level 356 includes adetermination of a quality level of the preliminary answers 354 based onthe answer rules 322. The inferred question information 352 may furtherbe associated with time information 348, where the time informationincludes one or more of current real-time, a time reference associatedwith entity submitting a request, and a time reference of a collectionsresponse.

When the IEI control module 308 determines that the answer quality level356 is below an answer quality threshold level, the IEI control module308 facilitates collecting of further content (e.g., by issuing acollections request 132 and receiving corresponding collectionsresponses 134 for analysis). When the answer quality level 356 comparesfavorably to the answer quality threshold level, the IEI control module308 issues an IEI response 246 based on the preliminary answers 354.When receiving training information 358, the IEI control module 308facilitates updating of one or more of the lists 330 and the rules 316and stores the updated list 330 and the updated rules 316 in thememories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzingcontent within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The methodincludes step 370 where a processing module of one or more processingmodules of one or more computing devices of the computing systemfacilitates updating of one or more rules and lists based on one or moreof received training information and received content. For example, theprocessing module updates rules with received rules to produce updatedrules and updates element lists with received elements to produceupdated element lists. As another example, the processing moduleinterprets the received content to identify a new word for at leasttemporary inclusion in the updated element list.

The method continues at step 372 where the processing module transformsat least some of the received content into formatted content. Forexample, the processing module processes the received content inaccordance with a transformation approach to produce the formattedcontent, where the formatted content supports compatibility withsubsequent element identification (e.g., typical sentence structures ofgroups of words).

The method continues at step 374 where the processing module processesthe formatted content based on the rules and the lists to produceidentified element information and/or an identified element information.For example, the processing module compares the formatted content toelement lists to identify a match producing identifiers for identifiedelements or new identifiers for unidentified elements when there is nomatch.

The method continues at step 376 with a processing module processes theidentified element information based on rules, the lists, and questioninformation to produce interpreted information. For example, theprocessing module compares the identified element information toassociated groups of representations of things to generate potentiallyvalid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processesthe interpreted information based on the rules, the questioninformation, and inferred question information to produce preliminaryanswers. For example, the processing module matches the interpretedinformation to one or more answers (e.g., embedded knowledge based on afact base built from previously received content) with highestcorrectness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generatesan answer quality level based on the preliminary answers, the rules, andthe inferred question information. For example, the processing modulepredicts the answer correctness likelihood level based on the rules, theinferred question information, and the question information. The methodbranches to step 384 when the answer quality level is favorable and themethod continues to step 382 when the answer quality level isunfavorable. For example, the generating of the answer quality levelfurther includes the processing module indicating that the answerquality level is favorable when the answer quality level is greater thanor equal to a minimum answer quality threshold level. As anotherexample, the generating of the answer quality level further includes theprocessing module indicating that the answer quality level isunfavorable when the answer quality level is less than the minimumanswer quality threshold level.

When the answer quality level is unfavorable, the method continues atstep 382 where the processing module facilitates gathering clarifyinginformation. For example, the processing module issues a collectionsrequest to facilitate receiving further content and or request questionclarification from a question requester. When the answer quality levelis favorable, the method continues at step 384 where the processingmodule issues a response that includes one or more answers based on thepreliminary answers and/or further updated preliminary answers based ongathering further content. For example, the processing module generatesa response that includes one or more answers and the answer qualitylevel and issues the response to the requester.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6A is a schematic block diagram of an embodiment of the elementidentification module 302 of FIG. 5A and the interpretation module 304of FIG. 5A. The element identification module 302 includes an elementmatching module 400 and an element grouping module 402. Theinterpretation module 304 includes a grouping matching module 404 and agrouping interpretation module 406. Generally, an embodiment of thisinvention presents solutions where the element identification module 302supports identifying potentially valid permutations of groupings ofelements while the interpretation module 304 interprets the potentiallyvalid permutations of groupings of elements to produce interpretedinformation that includes the most likely of groupings based on aquestion.

In an example of operation of the identifying of the potentially validpermutations of groupings of elements, when matching elements of theformatted content 314, the element matching module 400 generates matchedelements 412 (e.g., identifiers of elements contained in the formattedcontent 314) based on the element list 332. For example, the elementmatching module 400 matches a received element to an element of theelement list 332 and outputs the matched elements 412 to include anidentifier of the matched element. When finding elements that areunidentified, the element matching module 400 outputs un-recognizedwords information 408 (e.g., words not in the element list 332, maytemporarily add) as part of un-identified element information 342. Forexample, the element matching module 400 indicates that a match cannotbe made between a received element of the formatted content 314,generates the unrecognized words info 408 to include the receivedelement and/or a temporary identifier, and issues and updated elementlist 414 that includes the temporary identifier and the correspondingunidentified received element.

The element grouping module 402 analyzes the matched elements 412 inaccordance with element rules 318 to produce grouping error information410 (e.g., incorrect sentence structure indicators) when a structuralerror is detected. The element grouping module 402 produces identifiedelement information 340 when favorable structure is associated with thematched elements in accordance with the element rules 318. Theidentified element information 340 may further include groupinginformation of the plurality of permutations of groups of elements(e.g., several possible interpretations), where the grouping informationincludes one or more groups of words forming an associated set and/orsuper-group set of two or more subsets when subsets share a common coreelement.

In an example of operation of the interpreting of the potentially validpermutations of groupings of elements to produce the interpretedinformation, the grouping matching module 404 analyzes the identifiedelement information 340 in accordance with a groupings list 334 toproduce validated groupings information 416. For example, the groupingmatching module 404 compares a grouping aspect of the identified elementinformation 340 (e.g., for each permutation of groups of elements ofpossible interpretations), generates the validated groupings information416 to include identification of valid permutations aligned with thegroupings list 334. Alternatively, or in addition to, the groupingmatching module 404 generates an updated groupings list 418 whendetermining a new valid grouping (e.g., has favorable structure andinterpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validatedgroupings information 416 based on the question information 346 and inaccordance with the interpretation rules 320 to produce interpretedinformation 344 (e.g., most likely interpretations, next most likelyinterpretations, etc.). For example, the grouping interpretation module406 obtains context, obtains favorable historical interpretations,processes the validated groupings based on interpretation rules 320,where each interpretation is associated with a correctness likelihoodlevel.

FIG. 6B is a logic diagram of an embodiment of a method for interpretinginformation within a computing system. In particular, a method ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG.6B. The method includes step 430 where a processing module of one ormore processing modules of one or more computing devices of thecomputing system analyzes formatted content. For example, the processingmodule attempt to match a received element of the formatted content toone or more elements of an elements list. When there is no match, themethod branches to step 434 and when there is a match, the methodcontinues to step 432. When there is a match, the method continues atstep 432 where the processing module outputs matched elements (e.g., toinclude the matched element and/or an identifier of the matchedelement). When there is no match, the method continues at step 434 wherethe processing module outputs unrecognized words (e.g., elements and/ora temporary identifier for the unmatched element).

The method continues at step 436 where the processing module analyzesmatched elements. For example, the processing module attempt to match adetected structure of the matched elements (e.g., chained elements as ina received sequence) to favorable structures in accordance with elementrules. The method branches to step 440 when the analysis is unfavorableand the method continues to step 438 when the analysis is favorable.When the analysis is favorable matching a detected structure to thefavorable structure of the element rules, the method continues at step438 where the processing module outputs identified element information(e.g., an identifier of the favorable structure, identifiers of each ofthe detected elements). When the analysis is unfavorable matching adetected structure to the favorable structure of the element rules, themethod continues at step 440 where the processing module outputsgrouping error information (e.g., a representation of the incorrectstructure, identifiers of the elements of the incorrect structure, atemporary new identifier of the incorrect structure).

The method continues at step 442 where the processing module analyzesthe identified element information to produce validated groupingsinformation. For example, the processing module compares a groupingaspect of the identified element information and generates the validatedgroupings information to include identification of valid permutationsthat align with the groupings list. Alternatively, or in addition to,the processing module generates an updated groupings list whendetermining a new valid grouping.

The method continues at step 444 where the processing module interpretsthe validated groupings information to produce interpreted information.For example, the processing module obtains one or more of context andhistorical interpretations and processes the validated groupings basedon interpretation rules to generate the interpreted information, whereeach interpretation is associated with a correctness likelihood level(e.g., a quality level).

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 6C is a schematic block diagram of an embodiment of the answerresolution module 306 of FIG. 5A that includes an interim answer module460, and answer prioritization module 462, and a preliminary answerquality module 464. Generally, an embodiment of this invention presentssolutions where the answer resolution module 306 supports producing ananswer for interpreted information 344.

In an example of operation of the providing of the answer, the interimanswer module 460 analyzes the interpreted information 344 based onquestion information 346 and inferred question information 352 toproduce interim answers 466 (e.g., answers to stated and/or inferredquestions without regard to rules that is subject to furtherrefinement). The answer prioritization module 462 analyzes the interimanswers 466 based on answer rules 322 to produce preliminary answer 354.For example, the answer prioritization module 462 identifies allpossible answers from the interim answers 466 that conform to the answerrules 322.

The preliminary answer quality module 464 analyzes the preliminaryanswers 354 in accordance with the question information 346, theinferred question information 352, and the answer rules 322 to producean answer quality level 356. For example, for each of the preliminaryanswers 354, the preliminary answer quality module 464 may compare a fitof the preliminary answer 354 to a corresponding previous answer andquestion quality level, calculate the answer quality level 356 based ona level of conformance to the answer rules 322, calculate the answerquality level 356 based on alignment with the inferred questioninformation 352, and determine the answer quality level 356 based on aninterpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing ananswer within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. Themethod includes step 480 where a processing module of one or moreprocessing modules of one or more computing devices of the computingsystem analyzes received interpreted information based on questioninformation and inferred question information to produce one or moreinterim answers. For example, the processing module generates potentialanswers based on patterns consistent with previously produced knowledgeand likelihood of correctness.

The method continues at step 482 where the processing module analyzesthe one or more interim answers based on answer rules to producepreliminary answers. For example, the processing module identifies allpossible answers from the interim answers that conform to the answerrules. The method continues at step 484 where the processing moduleanalyzes the preliminary answers in accordance with the questioninformation, the inferred question information, and the answer rules toproduce an answer quality level. For example, for each of the elementaryanswers, the processing module may compare a fit of the preliminaryanswer to a corresponding previous answer-and-answer quality level,calculate the answer quality level based on performance to the answerrules, calculate answer quality level based on alignment with theinferred question information, and determine the answer quality levelbased on interpreted correlation with the question information.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 7A is an information flow diagram for interpreting informationwithin a computing system, where sets of entigens 504 are interpretedfrom sets of identigens 502 which are interpreted from sentences ofwords 500. Such identigen entigen intelligence (ID) processing of thewords (e.g., to ID process) includes producing one or more of interimknowledge, a preliminary answer, and an answer quality level. Forexample, the ID processing includes identifying permutations ofidentigens of a phrase of a sentence (e.g., interpreting humanexpressions to produce identigen groupings for each word of ingestedcontent), reducing the permutations of identigens (e.g., utilizing rulesto eliminate unfavorable permutations), mapping the reduced permutationsof identigens to at least one set of entigens (e.g., most likelyidentigens become the entigens) to produce the interim knowledge,processing the knowledge in accordance with a knowledge database (e.g.,comparing the set of entigens to the knowledge database) to produce apreliminary answer, and generating the answer quality level based on thepreliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about thereal world. The real-world includes items, actions, and attributes. Thehuman expressions include textual words, textual symbols, images, andother sensorial information (e.g., sounds). It is known that many words,within a given language, can mean different things based on groupingsand orderings of the words. For example, the sentences of words 500 caninclude many different forms of sentences that mean vastly differentthings even when the words are very similar.

The present invention presents solutions where the computing system 10supports producing a computer-based representation of a truest meaningpossible of the human expressions given the way that multitudes of humanexpressions relate to these meanings. As a first step of the flowdiagram to transition from human representations of things to a mostprecise computer representation of the things, the computer identifiesthe words, phrases, sentences, etc. from the human expressions toproduce the sets of identigens 502. Each identigen includes anidentifier of their meaning and an identifier of an instance for eachpossible language, culture, etc. For example, the words car andautomobile share a common meaning identifier but have different instanceidentifiers since they are different words and are spelled differently.As another example, the word duck is associated both with a bird and anaction to elude even though they are spelled the same. In this examplethe bird duck has a different meaning than the elude duck and as sucheach has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from humanrepresentations of things to the most precise computer representation ofthe things, the computer extracts meaning from groupings of theidentified words, phrases, sentences, etc. to produce the sets ofentigens 504. Each entigen includes an identifier of a singleconceivable and perceivable thing in space and time (e.g., independentof language and other aspects of the human expressions). For example,the words car and automobile are different instances of the same meaningand point to a common shared entigen. As another example, the word duckfor the bird meaning has an associated unique entigen that is differentthan the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressionsof things to the most precise computer representation of the things, thecomputer reasons facts from the extracted meanings. For example, thecomputer maintains a fact-based of the valid meanings from the validgroupings or sets of entigens so as to support subsequent inferences,deductions, rationalizations of posed questions to produce answers thatare aligned with a most factual view. As time goes on, and as an entigenhas been identified, it can encounter an experience transformations intime, space, attributes, actions, and words which are used to identifyit without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment ofrelationships between things 510 and representations of things 512within a computing system. The things 510 includes conceivable andperceivable things including actions 522, items 524, and attributes 526.The representation of things 512 includes representations of things usedby humans 514 and representation of things used by of computing devices516 of embodiments of the present invention. The things 510 relates tothe representations of things used by humans 514 where the inventionpresents solutions where the computing system 10 supports mapping therepresentations of things used by humans 514 to the representations ofthings used by computing devices 516, where the representations ofthings used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words528, textual symbols 530, images (e.g., non-textual) 532, and othersensorial information 534 (e.g., sounds, sensor data, electrical fields,voice inflections, emotion representations, facial expressions,whistles, etc.). The representations of things used by computing devices516 includes identigens 518 and entigens 520. The representations ofthings used by humans 514 maps to the identigens 518 and the identigens518 map to the entigens 520. The entigens 520 uniquely maps back to thethings 510 in space and time, a truest meaning the computer is lookingfor to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used byhumans 514 to the identigens 518, the identigens 518 is partitioned intoactenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548(e.g., attributes), and functionals 550 (e.g., that join and/ordescribe). Each of the actenyms 544, itenyms 546, and attrenyms 548 maybe further classified into singulatums 552 (e.g., identify one uniqueentigen) and pluratums 554 (e.g., identify a plurality of entigens thathave similarities).

Each identigen 518 is associated with an identigens identifier (IDN)536. The IDN 536 includes a meaning identifier (ID) 538 portion, aninstance ID 540 portion, and a type ID 542 portion. The meaning ID 538includes an identifier of common meaning. The instance ID 540 includesan identifier of a particular word and language. The type ID 542includes one or more identifiers for actenyms, itenyms, attrenyms,singulatums, pluratums, a time reference, and any other reference todescribe the IDN 536. The mapping of the representations of things usedby humans 514 to the identigens 518 by the computing system of thepresent invention includes determining the identigens 518 in accordancewith logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where theidentigens 518 map to the entigens 520. Multiple identigens may map to acommon unique entigen. The mapping of the identigens 518 to the entigens520 by the computing system of the present invention includesdetermining entigens in accordance with logic and instructions forforming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570within a computing system, where the synonym words table 570 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the synonym words table 570 to map textualwords 572 to identigens 518 and map the identigens 518 to entigens 520.For example, the words car, automobile, auto, bil (Swedish), carro(Spanish), and bil (Danish) all share a common meaning but are differentinstances (e.g., different words and languages). The words map to acommon meaning ID but to individual unique instant identifiers. Each ofthe different identigens map to a common entigen since they describe thesame thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576within a computing system, where the polysemous words table 576 includesmultiple fields including textual words 572, identigens 518, andentigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538 and the instance ID 540. The computing system of thepresent invention may utilize the polysemous words table 576 to maptextual words 572 to identigens 518 and map the identigens 518 toentigens 520. For example, the word duck maps to four differentidentigens since the word duck has four associated different meanings(e.g., bird, fabric, to submerge, to elude) and instances. Each of theidentigens represent different things and hence map to four differententigens.

FIG. 7E is a diagram of an embodiment of transforming words intogroupings within a computing system that includes a words table 580, agroupings of words section to validate permutations of groupings, and agroupings table 584 to capture the valid groupings. The words table 580includes multiple fields including textual words 572, identigens 518,and entigens 520. The identigens 518 includes fields for the meaningidentifier (ID) 538, the instance ID 540, and the type ID 542. Thecomputing system of the present invention may utilize the words table580 to map textual words 572 to identigens 518 and map the identigens518 to entigens 520. For example, the word pilot may refer to a flyerand the action to fly. Each meaning has a different identigen anddifferent entigen.

The computing system the present invention may apply rules to the fieldsof the words table 580 to validate various groupings of words. Thosethat are invalid are denoted with a “X” while those that are valid areassociated with a check mark. For example, the grouping “pilot Tom” isinvalid when the word pilot refers to flying and Tom refers to a person.The identigen combinations for the flying pilot and the person Tom aredenoted as invalid by the rules. As another example, the grouping “pilotTom” is valid when the word pilot refers to a flyer and Tom refers tothe person. The identigen combinations for the flyer pilot and theperson Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID586, word strings 588, identigens 518, and entigens 520. The computingsystem of the present invention may produce the groupings table 584 as astored fact base for valid and/or invalid groupings of words identifiedby their corresponding identigens. For example, the valid grouping“pilot Tom” referring to flyer Tom the person is represented with agrouping identifier of 3001 and identity and identifiers 150.001 and457.001. The entigen field 520 may indicate associated entigens thatcorrespond to the identigens. For example, entigen e717 corresponds tothe flyer pilot meaning and entigen e61 corresponds to the time theperson meaning. Alternatively, or in addition to, the entigen field 520may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string.Different ordering of the same words can produce multiple differentstrings and even different meanings and hence entigens. More broadly,each entry (e.g., role) of the groupings table 584 may refer togroupings of words, two or more word strings, an idiom, just identigens,just entigens, and/or any combination of the preceding elements. Eachentry has a unique grouping identifier. An idiom may have a uniquegrouping ID and include identifiers of original word identigens andreplacing identigens associated with the meaning of the idiom not justthe meaning of the original words. Valid groupings may still haveambiguity on their own and may need more strings and/or context toselect a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within acomputing system, where a computing device, at a time=t0, ingests andprocesses facts 598 at a step 590 based on rules 316 and fact baseinformation 600 to produce groupings 602 for storage in a fact base 592(e.g., words, phrases, word groupings, identigens, entigens, qualitylevels). The facts 598 may include information from books, archive data,Central intelligence agency (CIA) world fact book, trusted content, etc.The ingesting may include filtering to organize and promote better validgroupings detection (e.g., considering similar domains together). Thegroupings 602 includes one or more of groupings identifiers, identigenidentifiers, entigen identifiers, and estimated fit quality levels. Theprocessing step 590 may include identifying identigens from words of thefacts 598 in accordance with the rules 316 and the fact base info 600and identifying groupings utilizing identigens in accordance with rules316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish thefact base 592, at a time=t1+, the computing device ingests and processesnew content 604 at a step 594 in accordance with the rules 316 and thefact base information 600 to produce preliminary grouping 606. The newcontent may include updated content (e.g., timewise) from periodicals,newsfeeds, social media, etc. The preliminary grouping 606 includes oneor more of preliminary groupings identifiers, preliminary identigenidentifiers, preliminary entigen identifiers, estimated fit qualitylevels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step596 based on the rules 316 and the fact base info 600 to produce updatedfact base info 608 for storage in the fact base 592. The validatingincludes one or more of reasoning a fit of existing fact base info 600with the new preliminary grouping 606, discarding preliminary groupings,updating just time frame information associated with an entry of theexisting fact base info 600 (e.g., to validate knowledge for thepresent), creating new entigens, and creating a median entigen tosummarize portions of knowledge within a median indicator as a qualitylevel indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns thatdevelop by themselves instead of searching for patterns as in prior artartificial intelligence systems. Growth of the fact base 592 enablessubsequent reasoning to create new knowledge including deduction,induction, inference, and inferential sentiment (e.g., a chain ofsentiment sentences). Examples of sentiments includes emotion, beliefs,convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within acomputing system. The groupings table 620 includes multiple fieldsincluding grouping ID 586, word strings 588, an IF string 622 and a THENstring 624. Each of the fields for the IF string 622 and the THEN string624 includes fields for an identigen (IDN) string 626, and an entigen(ENI) string 628. The computing system of the present invention mayproduce the groupings table 620 as a stored fact base to enable IF THENbased inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someonehas a tumor, THEN someone is sick and the grouping 5494 points of thelogic that IF someone is sick, THEN someone is sad. As a result ofutilizing inference, the new knowledge inference 630 may producegrouping 5495 where IF someone has a tumor, THEN someone is possibly sad(e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizingaccumulated knowledge within a computing system, where a computingdevice ingests and processes question information 346 at a step 640based on rules 316 and fact base info 600 from a fact base 592 toproduce preliminary grouping 606. The ingesting and processing questionsstep 640 includes identifying identigens from words of a question inaccordance with the rules 316 and the fact base information 600 and mayalso include identifying groupings from the identified identigens inaccordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step596 based on the rules 316 and the fact base information 600 to produceidentified element information 340. For example, the computing devicereasons fit of existing fact base information with new preliminarygroupings 606 to produce the identified element information 340associated with highest quality levels. The computing device interpretsa question of the identified element information 340 at a step 642 basedon the rules 316 and the fact base information 600. The interpreting ofthe question may include separating new content from the question andreducing the question based on the fact base information 600 and the newcontent.

The computing device produces preliminary answers 354 from theinterpreted information 344 at a resolve answer step 644 based on therules 316 and the fact base information 600. For example, the computingdevice compares the interpreted information 344 two the fact baseinformation 600 to produce the preliminary answers 354 with highestquality levels utilizing one or more of deduction, induction,inferencing, and applying inferential sentiments logic. Alternatively,or in addition to, the computing device may save new knowledgeidentified from the question information 346 to update the fact base592.

FIG. 8D is a data flow diagram for answering questions utilizinginterference within a computing system that includes a groupings table648 and the resolve answer step 644 of FIG. 8C. The groupings table 648includes multiple fields including fields for a grouping (GRP)identifier (ID) 586, word strings 588, an identigen (IDN) string 626,and an entigen (ENI) 628. The groupings table 648 may be utilized tobuild a fact base to enable resolving a future question into an answer.For example, the grouping 8356 notes knowledge that Michael sleeps eighthours and grouping 8357 notes that Michael usually starts to sleep at11:00 PM.

In a first question example that includes a question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time is1:00 AM to produce a preliminary answer of “possibly YES” when inferringthat Michael is probably sleeping at 1:00 AM when Michael usually startssleeping at 11:00 PM and Michael usually sleeps for a duration of eighthours.

In a second question example that includes the question “Michaelsleeping?”, the resolve answer step 644 analyzes the question from theinterpreted information 344 in accordance with the fact base information600, the rules 316, and a real-time indicator that the current time isnow 11:00 AM to produce a preliminary answer of “possibly NO” wheninferring that Michael is probably not sleeping at 11:00 AM when Michaelusually starts sleeping at 11:00 PM and Michael usually sleeps for aduration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodimentof relationships between things and representations of things within acomputing system. While things in the real world are described withwords, it is often the case that a particular word has multiple meaningsin isolation.

Interpreting the meaning of the particular word may hinge on analyzinghow the word is utilized in a phrase, a sentence, multiple sentences,paragraphs, and even whole documents or more. Describing and stratifyingthe use of words, word types, and possible meanings help in interpretinga true meaning.

Humans utilize textual words 528 to represent things in the real world.Quite often a particular word has multiple instances of differentgrammatical use when part of a phrase of one or more sentences. Thegrammatical use 649 of words includes the nouns and the verbs, and alsoincludes adverbs, adjectives, pronouns, conjunctions, prepositions,determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the Englishlanguage can be utilized as a noun or a verb. For instance, whenutilized as a noun, the word “bat” may apply to a baseball bat or mayapply to a flying “bat.” As another instance, when utilized as a verb,the word “bat” may apply to the action of hitting or batting an object,i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type(e.g., type identifier 542). The word types include objects (e.g., items524), characteristics (e.g., attributes 526), actions 522, and thefunctionals 550 for joining other words and describing words. Forexample, when the word “bat” is utilized as a noun, the word isdescribing the object of either the baseball bat or the flying bat. Asanother example, when the word “bat” is utilized as a verb, the word isdescribing the action of hitting.

To determine possible meanings, the words, by word type, are mapped toassociative meanings (e.g., identigens 518). For each possibleassociative meaning, the word type is documented with the meaning andfurther with an identifier (ID) of the instance (e.g., an identigenidentifier).

For the example of the word “bat” when utilized as a noun for thebaseball bat, a first identigen identifier 536-1 includes a type ID542-1 associated with the object 524, an instance ID 540-1 associatedwith the first identigen identifier (e.g., unique for the baseball bat),and a meaning ID 538-1 associated with the baseball bat. For the exampleof the word “bat” when utilized as a noun for the flying bat, a secondidentigen identifier 536-2 includes a type ID 542-1 associated with theobject 524, an instance ID 540-2 associated with the second identigenidentifier (e.g., unique for the flying bat), and a meaning ID 538-2associated with the flying bat. For the example of the word “bat” whenutilized as a verb for the bat that hits, a third identigen identifier536-2 includes a type ID 542-2 associated with the actions 522, aninstance ID 540-3 associated with the third identigen identifier (e.g.,unique for the bat that hits), and a meaning ID 538-3 associated withthe bat that hits.

With the word described by a type and possible associative meanings, acombination of full grammatical use of the word within the phrase etc.,application of rules, and utilization of an ever-growing knowledgedatabase that represents knowledge by linked entigens, the absolutemeaning (e.g., entigen 520) of the word is represented as a uniqueentigen. For example, a first entigen e1 represents the absolute meaningof a baseball bat (e.g., a generic baseball bat not a particularbaseball bat that belongs to anyone), a second entigen e2 represents theabsolute meaning of the flying bat (e.g., a generic flying bat not aparticular flying bat), and a third entigen e3 represents the absolutemeaning of the verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings forstorage in a knowledge database are discussed in greater detail withreference to FIGS. 8F-H. Those embodiments further discuss thediscerning of the grammatical use, the use of the rules, and theutilization of the knowledge database to definitively interpret theabsolute meaning of a string of words.

Another embodiment of methods to respond to a query to produce an answerbased on knowledge stored in the knowledge database are discussed ingreater detail with reference to FIGS. 8J-L. Those embodiments furtherdiscuss the discerning of the grammatical use, the use of the rules, andthe utilization of the knowledge database to interpret the query. Thequery interpretation is utilized to extract the answer from theknowledge database to facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SSmemory 96 of FIG. 2. Generally, an embodiment of this invention providespresents solutions where the computing system 10 supports processingcontent to produce knowledge for storage in a knowledge database.

The processing of the content to produce the knowledge includes a seriesof steps. For example, a first step includes identifying words of aningested phrase to produce tokenized words. As depicted in FIG. 8F, aspecific example of the first step includes the content ingestion module300 comparing words of source content 310 to dictionary entries toproduce formatted content 314 that includes identifiers of known words.Alternatively, when a comparison is unfavorable, the temporaryidentifier may be assigned to an unknown word. For instance, the contentingestion module 300 produces identifiers associated with the words“the”, “black”, “bat”, “eats”, and “fruit” when the ingested phraseincludes “The black bat eats fruit”, and generates the formatted content314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledgeincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action. As depicted in FIG. 8F, aspecific example of the second step includes the element identificationmodule 302 performing a look up of identigen identifiers, utilizing anelement list 332 and in accordance with element rules 318, of the one ormore identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, thecharacteristic, and the action (OCA) associated with the tokenized word(e.g. sequential identigens). For instance, the element identificationmodule 302 identifies a functional symbol for “the”, identifies a singleidentigen for “black”, identifies two identigens for “bat” (e.g.,baseball bat and flying bat), identifies a single identigen for “eats”,and identifies a single identigen for “fruit.” When at least onetokenized word is associated with multiple identigens, two or morepermutations of sequential combinations of identigens for each tokenizedword result. For example, when “bat” is associated with two identigens,two permutations of sequential combinations of identigens result for theingested phrase.

A third step of the processing of the content to produce the knowledgeincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation), where the equation package includes a sequentiallinking of pairs of identigens (e.g., relationships), where eachsequential linking pairs a preceding identigen to a next identigen, andwhere an equation element describes a relationship between pairedidentigens (OCAs) such as describes, acts on, is a, belongs to, did, didto, etc. Multiple OCAs occur for a common word when the word hasmultiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340, theinterpretation module 304 generates, in accordance with interpretationrules 320 and a groupings list 334, an equation package to include oneor more of the identifiers of the tokenized words, a list of identifiersof the identigens of the equation package, a list of pairing identifiersfor sequential pairs of identigens, and a quality metric associated witheach sequential pair of identigens (e.g., likelihood of a properinterpretation). For instance, the interpretation module 304 produces afirst equation package that includes a first identigen pairing of ablack bat (e.g., flying bat with a higher quality metric level), thesecond pairing of bat eats (e.g., the flying bat eats, with a higherquality metric level), and a third pairing of eats fruit, and theinterpretation module 304 produces a second equation package thatincludes a first pairing of a black bat (e.g., baseball bat, with aneutral quality metric level), the second pairing of bat eats (e.g., thebaseball bat eats, with a lower quality metric level), and a thirdpairing of eats fruit.

A fourth step of the processing of the content to produce the knowledgeincludes selecting a surviving equation package associated with a mostfavorable confidence level. As depicted in FIG. 8F, a specific exampleof the fourth step includes the interpretation module 304 applyinginterpretation rules 320 (i.e., inference, pragmatic engine, utilizingthe identifiers of the identigens to match against known validcombinations of identifiers of entigens) to reduce a number ofpermutations of the sequential combinations of identigens to produceinterpreted information 344 that includes identification of at least oneequation package as a surviving interpretation SI (e.g., higher qualitymetric level).

Non-surviving equation packages are eliminated that compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation NSI 2 (e.g.,lower quality metric level), where an overall quality metric level maybe assigned to each equation package based on quality metric levels ofeach pairing, such that a higher quality metric level of an equationpackage indicates a higher probability of a most favorableinterpretation. For instance, the interpretation module 304 eliminatesthe equation package that includes the second pairing indicating thatthe “baseball bat eats” which is inconsistent with a desired qualitymetric level of one or more of the groupings list 334 and theinterpretation rules 320 and selects the equation package associatedwith the “flying bat eats” which is favorably consistent with the one ormore of the quality metric levels of the groupings list 334 and theinterpretation rules 320.

A fifth step of the processing of the content to produce the knowledgeutilizing the confidence level includes integrating knowledge of thesurviving equation package into a knowledge database. For example,integrating at least a portion of the reduced OCA combinations into agraphical database to produce updated knowledge. As another example, theportion of the reduced OCA combinations may be translated into rows andcolumns entries when utilizing a rows and columns database rather than agraphical database. When utilizing the rows and columns approach for theknowledge database, subsequent access to the knowledge database mayutilize structured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includesthe ID control module 308 recovering fact base information 600 from SSmemory 96 to identify a portion of the knowledge database for potentialmodification utilizing the OCAs of the surviving interpretation SI 1(i.e., compare a pattern of relationships between the OCAs of thesurviving interpretation SI 1 from the interpreted information 344 torelationships of OCAs of the portion of the knowledge database includingpotentially new quality metric levels).

The fifth step further includes determining modifications (e.g.,additions, subtractions, further clarifications required wheninformation is complex, etc.) to the portion of the knowledge databasebased on the new quality metric levels. For instance, the IEI controlmodule 308 causes adding the element “black” as a “describes”relationship of an existing bat OCA and adding the element “fruit” as aneats “does to” relationship to implement the modifications to theportion of the fact base information 600 to produce updated fact baseinformation 608 for storage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processingcontent to produce knowledge for storage within a computing system. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-8E,8F, and also FIG. 8G. The method includes step 650 where a processingmodule of one or more processing modules of one or more computingdevices of the computing system identifies words of an ingested phraseto produce tokenized words. The identified includes comparing words toknown words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where theprocessing module identifies one or more identigens that corresponds tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action (e.g., OCA). The identifying includesperforming a lookup of identifiers of the one or more identigensassociated with each tokenized word, where the different identifiersassociated with each of the potential object, the characteristic, andthe action associated with the tokenized word.

The method continues at step 652 where the processing module, for eachpermutation of sequential combinations of identigens, generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens, where each sequential linkingpairs a preceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing modulegenerates the equation package to include a plurality of equationelements, where each equation element describes the relationship (e.g.,describes, acts on, is a, belongs to, did, did too, etc.) betweensequentially adjacent identigens of a plurality of sequentialcombinations of identigens. Each equation element may be furtherassociated with a quality metric to evaluate a favorability level of aninterpretation in light of the sequence of identigens of the equationpackage.

The method continues at step 653 where the processing module selects asurviving equation package associated with most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens),to reduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package, wherenon-surviving equation packages are eliminated the compare unfavorablyto pairing rules and/or are associated with an unfavorable qualitymetric levels to produce a non-surviving interpretation, where anoverall quality metric level is assigned to each equation package basedon quality metric levels of each pairing, such that a higher qualitymetric level indicates an equation package with a higher probability offavorability of correctness.

The method continues at step 654 where the processing module integratesknowledge of the surviving equation package into a knowledge database.For example, the processing module integrates at least a portion of thereduced OCA combinations into a graphical database to produce updatedknowledge. The integrating may include recovering fact base informationfrom storage of the knowledge database to identify a portion of theknowledge database for potential modifications utilizing the OCAs of thesurviving equation package (i.e., compare a pattern of relationshipsbetween the OCAs of the surviving equation package to relationships ofthe OCAs of the portion of the knowledge database including potentiallynew quality metric levels). The integrating further includes determiningmodifications (e.g., additions, subtractions, further clarificationsrequired when complex information is presented, etc.) to produce theupdated knowledge database that is based on fit of acceptable qualitymetric levels, and implementing the modifications to the portion of thefact base information to produce the updated fact base information forstorage in the portion of the knowledge database.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of acomputing system that includes the content ingestion module 300 of FIG.5E, the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, andthe SS memory 96 of FIG. 2. Generally, an embodiment of this inventionprovides solutions where the computing system 10 supports for generatinga query response to a query utilizing a knowledge database.

The generating of the query response to the query includes a series ofsteps. For example, a first step includes identifying words of aningested query to produce tokenized words. As depicted in FIG. 8J, aspecific example of the first step includes the content ingestion module300 comparing words of query info 138 to dictionary entries to produceformatted content 314 that includes identifiers of known words. Forinstance, the content ingestion module 300 produces identifiers for eachword of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the queryincludes, for each tokenized word, identifying one or more identigensthat correspond the tokenized word, where each identigen describes oneof an object, a characteristic, and an action (OCA). As depicted in FIG.8J, a specific example of the second step includes the elementidentification module 302 performing a look up of identifiers, utilizingan element list 332 and in accordance with element rules 318, of the oneor more identigens associated with each tokenized word of the formattedcontent 314 to produce identified element information 340. A uniqueidentifier is associated with each of the potential object, thecharacteristic, and the action associated with a particular tokenizedword. For instance, the element identification module 302 produces asingle identigen identifier for each of the black color, an animal,flies, eats, fruit, and insects.

A third step of the generating of the query response to the queryincludes, for each permutation of sequential combinations of identigens,generating a corresponding equation package (i.e., candidateinterpretation). The equation package includes a sequential linking ofpairs of identigens, where each sequential linking pairs a precedingidentigen to a next identigen. An equation element describes arelationship between paired identigens (OCAs) such as describes, actson, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includesthe interpretation module 304, for each permutation of identigens ofeach tokenized word of the identified element information 340,generating the equation packages in accordance with interpretation rules320 and a groupings list 334 to produce a series of equation elementsthat include pairings of identigens. For instance, the interpretationmodule 304 generates a first pairing to describe a black animal, asecond pairing to describe an animal that flies, a third pairing todescribe flies and eats, a fourth pairing to describe eats fruit, and afifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includesselecting a surviving equation package associated with a most favorableinterpretation. As depicted in FIG. 8J, a specific example of the fourthstep includes the interpretation module 304 applying the interpretationrules 320 (i.e., inference, pragmatic engine, utilizing the identifiersof the identigens to match against known valid combinations ofidentifiers of entigens) to reduce the number of permutations of thesequential combinations of identigens to produce interpreted information344. The interpreted information 344 includes identification of at leastone equation package as a surviving interpretation SI 10, wherenon-surviving equation packages, if any, are eliminated that compareunfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includesutilizing a knowledge database, generating a query response to thesurviving equation package of the query, where the surviving equationpackage of the query is transformed to produce query knowledge forcomparison to a portion of the knowledge database. An answer isextracted from the portion of the knowledge database to produce thequery response.

As depicted in FIG. 8K, a specific example of the fifth step includesthe answer resolution module 306 interpreting the survivinginterpretation SI 10 of the interpreted information 344 in accordancewith answer rules 322 to produce query knowledge QK 10 (i.e., agraphical representation of knowledge when the knowledge databaseutilizes a graphical database). For example, the answer resolutionmodule 306 accesses fact base information 600 from the SS memory 96 toidentify the portion of the knowledge database associated with afavorable comparison of the query knowledge QK 10 (e.g., by comparingattributes of the query knowledge QK 10 to attributes of the fact baseinformation 600), and generates preliminary answers 354 that includesthe answer to the query. For instance, the answer is “bat” when theassociated OCAs of bat, such as black, eats fruit, eats insects, is ananimal, and flies, aligns with OCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating aquery response to a query utilizing knowledge within a knowledgedatabase within a computing system. In particular, a method is presentedfor use in conjunction with one or more functions and features describedin conjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The methodincludes step 655 where a processing module of one or more processingmodules of one or more computing devices of the computing systemidentifies words of an ingested query to produce tokenized words. Forexample, the processing module compares words to known words ofdictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 656 where theprocessing module identifies one or more identigens that correspond tothe tokenized word, where each identigen describes one of an object, acharacteristic, and an action. For example, the processing moduleperforms a lookup of identifiers of the one or more identigensassociated with each tokenized word, where different identifiersassociated with each permutation of a potential object, characteristic,and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, themethod continues at step 657 where the processing module generates aplurality of equation elements to form a corresponding equation package,where each equation element describes a relationship betweensequentially linked pairs of identigens. Each sequential linking pairs apreceding identigen to a next identigen. For example, for eachpermutation of identigens of each tokenized word, the processing moduleincludes all other permutations of all other tokenized words to generatethe equation packages. Each equation package includes a plurality ofequation elements describing the relationships between sequentiallyadjacent identigens of a plurality of sequential combinations ofidentigens.

The method continues at step 658 where the processing module selects asurviving equation package associated with a most favorableinterpretation. For example, the processing module applies rules (i.e.,inference, pragmatic engine, utilizing the identifiers of the identigensto match against known valid combinations of identifiers of entigens) toreduce the number of permutations of the sequential combinations ofidentigens to identify at least one equation package. Non-survivingequation packages are eliminated the compare unfavorably to pairingrules.

The method continues at step 659 where the processing module generates aquery response to the surviving equation package, where the survivingequation package is transformed to produce query knowledge for locatingthe portion of a knowledge database that includes an answer to thequery. As an example of generating the query response, the processingmodule interprets the surviving the equation package in accordance withanswer rules to produce the query knowledge (e.g., a graphicalrepresentation of knowledge when the knowledge database utilizes agraphical database format).

The processing module accesses fact base information from the knowledgedatabase to identify the portion of the knowledge database associatedwith a favorable comparison of the query knowledge (e.g., favorablecomparison of attributes of the query knowledge to the portion of theknowledge database, aligning favorably comparing entigens withoutconflicting entigens). The processing module extracts an answer from theportion of the knowledge database to produce the query response.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the computing system 10of FIG. 1 or by other devices. In addition, at least one memory section(e.g., a computer readable memory, a non-transitory computer readablestorage medium, a non-transitory computer readable memory organized intoa first memory element, a second memory element, a third memory element,a fourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 9A-9C are schematic block diagrams of another embodiment of acomputing system illustrating a method for determining meaning of aphrase with ambiguity resolution within the computing system. Thecomputing system includes the content ingestion module 300 of FIG. 5E,the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, anda knowledge database 700. The knowledge database 700 may be implementedutilizing one or more of the memories of FIG. 2.

FIG. 9A illustrates example steps of the method of operation for thedetermining meaning of the phrase with ambiguity resolution, where, in afirst step the content ingestion module 300 obtains a query 702 thatincludes a string of words. The obtaining includes receiving, lookingup, retrieving, and generating. For example, the content ingestionmodule 300 receives the query 702 that includes a string of words“George green?”.

Having obtained the query 702, a second step of the method fordetermining meaning of the phrase includes the content ingestion module300 parsing the query 702 to generate query words 704. The parsingincludes one or more of validating words of the string of words withvalid words of a dictionary 703, eliminating unneeded words, replacingundesired words with replacement words that are desired, and addingadditional words that may provide enhanced clarity of understanding asis commonly used within a given language. For example, the contentingestion module 300 obtains the dictionary 703 from the knowledgedatabase 700 and validates the words of the string of words with wordsof the dictionary 703 to produce the query words 704. For instance, thecontent ingestion module 300 compares each word of the string of words“George green?” to words of the dictionary 703 to validate each of thewords producing the valid sequence of query words 704 that includes“George, green.”

In a third step of the method for the determining meaning of the phrase,the element identification module 302 determines a set of identigens foreach query word of the query 702 to produce a plurality of sets ofidentigens 708. A set of identigens of the plurality of sets ofidentigens represents one or more different meanings of a query word ofthe query. Each identigen of the set of identigens includes a meaningidentifier, an instance identifier, and a time reference. Each meaningidentifier associated with a particular set of identigens represents adifferent meaning of one or more different meanings of a correspondingquery word of the query 702. Each time reference provides timeinformation when a corresponding different meaning of the one or moredifferent meanings is valid (e.g., a time range for a particular wordmeaning).

A first set of identigens of the plurality of sets of identigensincludes one or more different meanings of a first word of the validsequence of query words 704. The producing of the set of identigensincludes a series of sub-steps. A first sub-step includes accessing, foreach word of the valid sequence of query words 704, the knowledgedatabase 700 to recover the set of identigens. For example, the elementidentification module 302 receives identigen information 706 from theknowledge database 700 that includes an identigen set #1 for the word“George”, where the identigen set #1 includes identigen 20. Theidentigen 20 represents an interpretation of the word “George”,including “a person.” As another example, the element identificationmodule 302 receives identigen information 706 from the knowledgedatabase 700 that includes an identigen sets #2 for the word “green”,including two meanings of “inexperienced” and “sickly.”

A second sub-step includes interpreting a query response from anothercomputing device to produce the set of identigens, where the queryresponse is received in response to an identigen query with regards tothe word. For example, the element identification module 302 receivesthe plurality of sets of identigens from the other computing device.

FIG. 9B further illustrates the example steps of the method of operationfor the determining meaning of the phrase with ambiguity resolution,where, having produced the plurality of sets of identigens 708, in afourth step the interpretation module 304 interprets, based on identigenpairing rules of the knowledge database 700, pairs of sequentiallyadjacent identigens of adjacent sets of identigens of the plurality ofsets of identigens to determine a first most likely meaninginterpretation of the query and produce a first query entigen group of aplurality of query entigen groups 713. The plurality of query entigengroups 713 are associated with a plurality of most likely meaninginterpretations of the query. The plurality of most likely meaninginterpretations of the query 702 includes the first most likely meaninginterpretation of the query.

The first query entigen group represents the first most likely meaninginterpretation of the query. Each entigen of the first query entigengroup corresponds to a selected identigen of one of the plurality ofsets of identigens having a selected meaning identifier of the one ormore different meanings of a corresponding query word of the query wordsthat represents a first most likely meaning interpretation of thecorresponding query word. Each selected identigen corresponding to thefirst query entigen group favorably pairs with at least onecorresponding sequentially adjacent identigen of an adjacent set ofidentigens in accordance with the identigen pairing rules. For example,identigen #20 favorably pairs with identigen #31.

Each entigen of the first query entigen group represents a singleconceivable and perceivable thing in space and time that is independentof language and corresponds to a time reference of a correspondingselected identigen associated with the first query entigen group. Theknowledge database 700 includes a plurality of records that linkentigens having a connected meaning.

In an instance, the interpretation module 304 interprets identigen rules710 from the knowledge database 700 with regards to the identigens ofthe sets of identigens 708. For instance, the interpretation module 304interprets the identigen rules 710 to determine that the identigen #20is allowed to pair with identigens #31 and #32. The interpretationmodule 304 generates the first query entigen group to include identigens#20 and #31 and a second query entigen group to include identigens #20and #32.

The example of operation fourth step further includes the interpretationmodule 304 interpreting, based on the identigen pairing rules of theknowledge database, the pairs of sequentially adjacent identigens of theadjacent sets of identigens of the plurality of sets of identigens todetermine a second most likely meaning interpretation of the query andproduce the second query entigen group of the plurality of query entigengroups. For example, identigens #20 and #32 are selected to produce thesecond query entigen group.

The plurality of most likely meaning interpretations of the queryincludes the second most likely meaning interpretation of the query. Thesecond query entigen group represents the second most likely meaninginterpretation of the query. Each entigen of the second query entigengroup corresponds to a selected identigen of one of the plurality ofsets of identigens having a selected meaning identifier of the one ormore different meanings of a corresponding query word of the query wordsthat represents a second most likely meaning interpretation of thecorresponding query word. Each selected identigen corresponding to thesecond query entigen group favorably pairs with at least onecorresponding sequentially adjacent identigen of an adjacent set ofidentigens in accordance with the identigen pairing rules. For example,identigen #20 favorably pairs with identigen #32. Each entigen of thesecond query entigen group represents a single conceivable andperceivable thing in space and time that is independent of language andcorresponds to a time reference of a corresponding selected identigenassociated with the second query entigen group.

FIG. 9C further illustrates the example steps of the method of operationfor the determining meaning of the phrase with ambiguity resolution,where, having produced the plurality of query entigen groups 713, in afifth step the interpretation module 304 identifies an inconsistencybetween at least two query entigen groups of the plurality of queryentigen groups. The identifying the inconsistency between at least twoquery entigen groups of the plurality of query entigen groups includesone or more of a variety of approaches. A first approach includesidentifying a first query entigen of the first query entigen group thatis different than a first query entigen of the second query entigengroup. For example, the interpretation module 304 identifies entigens#31 and entigen #32 as different.

A second approach includes determining that a number of query entigensof the first query entigen group is different than a number of queryentigens of the second query entigen group. For example, in anotherinstance, the interpretation module 304 determines that to entigens arein the first query entigen group and 3 entigens are in the second queryentigen group.

Having identified the inconsistency, a sixth step of the example methodof operation includes the interpretation module 304 obtaining aninconsistency clarification based on the inconsistency between the atleast two query entigen groups of the plurality of query entigen groups.The obtaining the inconsistency clarification based on the inconsistencybetween the at least two query entigen groups of the plurality of queryentigen groups includes at least one of a variety of approaches. A firstapproach includes the interpretation module 304 accessing the knowledgedatabase 700 utilizing the first query entigen group to produce aclarification entigen group as the inconsistency clarification. Forexample, the interpretation module 304 interprets entigen information714 from the knowledge database 700 that indicates that entigens #20,#43, #32, and #54 compare favorably to the second query entigen group(e.g., entigens #20 and #32), which provides disconfirming knowledgethat George is sickly (e.g., since George's health is seldom sickly).The interpretation module 304 includes entigens #20, #43, #32, and #54in the clarification entigen group.

A second approach includes the interpretation module 304 interpreting aclarification response 718 to a clarification request 716 to produce theclarification entigen group as the inconsistency clarification. Theclarification request 716 is based on the inconsistency between the atleast two query entigen groups. For example, the interpretation module304 generates the clarification request 716 to include a question“George are you sickly?” to test the query entigen group entigen andoutputs the clarification request 716 to a query entity (e.g., acomputing device associated with a subject of the query, a proxy for thesubject, or someone asking the question). Having issued theclarification request 716, the interpretation module 304 obtains theclarification response 718 from the entity associated with the query(e.g., George). For example, the interpretation module 304 receives theclarification response 718 from George that includes “I'm not sick.”Having obtained the clarification response 718, the interpretationmodule 304 interprets the clarification response 718 to produce theclarification entigen group (e.g., entigens indicating that George isnot sick) as the inconsistency clarification.

Having obtained the inconsistency clarification, a seventh step of theexample method of operation includes the interpretation module 304selecting one query entigen group of the plurality of query entigengroups based on the inconsistency clarification to produce a final queryentigen group 715. The selecting the one query entigen group of theplurality of query entigen groups based on the inconsistencyclarification to produce the final query entigen group 715 includes aseries of steps. A first step includes the interpretation module 304obtaining a clarification entigen group based on the inconsistencyclarification. For example, the interpretation module 304 obtains theclarification entigen group that includes entigens number #20, #43, #32,and #54. As another example, the interpretation module obtains theclarification entigen group that includes entigens indicating thatGeorge is not sick.

A second step includes the interpretation module 304 selecting the onequery entigen group of the plurality of query entigen groups that ismost consistent with the clarification entigen group to produce thefinal query entigen group 715. For example, the interpretation module304 disqualifies the second query entigen group when utilizing theclarification entigen group that includes entigens indicating thatGeorge is not 6. As another example, the interpretation module 304disqualifies the second query entigen group when interpreting theclarification entigen group that includes entigens number #20, #43, #32,and #54 indicating that George is seldom sick. Having disqualified thesecond query entigen group, the interpretation module 304 deduces thatthe first query entigen group is the most consistent with theclarification entigen group. As such, the interpretation module 304selects the first query entigen group (e.g., that includes entigens #20for George and #31 for inexperienced).

Having produced the final query entigen group 715, the answer resolutionmodule 306 accesses the knowledge database 700 utilizing the final queryentigen group 715 to produce a response entigen group. For example, theanswer resolution module 306 interprets entigen information 714 from theknowledge database 700 that includes linked entigens #20 and #31 (e.g.,for the George entigen and the inexperienced entigen).

Having produced the response entigen group, the answer resolution module306 generates a response to the query utilizing the response entigengroup. The response includes at least one response word. For example,the answer resolution module 306 generates the response to the query toinclude “George is inexperienced” when the response entigen groupincludes the entigens for “George” and “inexperienced” (e.g., associatedwith George is inexperienced as an interpretation of “George is green”).

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element, a sixth memory elementetc.) that stores operational instructions can, when executed by one ormore processing modules of one or more computing devices (e.g., one ormore servers, one or more user devices) of the computing system 10,cause the one or more computing devices to perform any or all of themethod steps described above.

FIGS. 10A-10B are schematic block diagrams of another embodiment of acomputing system illustrating a method for processing informalutilization of words within the computing system. The computing systemincludes the content ingestion module 300 of FIG. 5E, the elementidentification module 302 of FIG. 5E, the interpretation module 304 ofFIG. 5E, the answer resolution module 306 of FIG. 5, and a knowledgedatabase 700. The knowledge database 700 may be implemented utilizingone or more of the memories of FIG. 2.

FIG. 10A illustrates example steps of the method of operation for theprocessing informal utilization of words, where, in a first step thecontent ingestion module 300 obtains a phrase 802 from a subject (e.g.,Tom) that includes a string of words. The obtaining includes receiving,looking up, retrieving, and generating. For example, the contentingestion module 300 receives the phrase 802 that includes a string ofwords from Tom “Fred is plumb crazy.”

Having obtained the phrase 802, a second step of the method for theprocessing informal utilization of words includes the content ingestionmodule 300 parsing the phrase 802 to generate phrase words 804. Theparsing includes one or more of validating words of the string of wordswith valid words of a dictionary 703, eliminating unneeded words,replacing undesired words with replacement words that are desired, andadding additional words that may provide enhanced clarity ofunderstanding as is commonly used within a given language. For example,the content ingestion module 300 obtains the dictionary 703 from theknowledge database 700 and validates the words of the string of wordswith words of the dictionary 703 to produce the phrase words 804. Forinstance, the content ingestion module 300 compares each word of thestring of words “Fred is plumb crazy” to words of the dictionary 703 tovalidate each of the words producing the valid sequence of phrase words804 that includes “Fred”, “plumb”, and “crazy.”

In a third step of the method for processing informal utilization ofwords, the element identification module 302 identifies a set ofidentigens for each valid word of the phrase words 804 to produce a(plurality) of sets of identigens 808. A first set of identigens of theplurality of sets of identigens includes one or more different meaningsof a first word of the valid sequence of phrase words.

The identifying the set of identigens includes a series of one or moresub-steps. A first sub-step includes accessing, for each word of thevalid sequence of phrase words 804, the knowledge database 700 torecover the set of identigens. For example, the element identificationmodule 302 receives identigen information 706 from the knowledgedatabase 700 that includes an identigen set #1 for the word “Fred”,where the identigen set #1 includes identigen #10 for Fred the person.As another example, the identigen information 706 also includes anidentigen set #2 for the word “plumb”, including “test”, “exactly”, and“plumb bob.” As yet another example, the element identification module302 receives identigen information 706 from the knowledge database 700that includes identigens 26, and 27 for the word “crazy”, whereidentigen 26 is associated with a meaning of deranged and identigen 27is associated with a meaning of enthusiastic.

The identigen information 706 also indicates when identigens areassociated with informal meanings. For example, identigen #22 associatedwith the meaning of exactly when the word “plumb” is utilized in aninformal way. As another example, identigen number 27 is associated withthe meaning of enthusiastic when the word “crazy” is utilized in aninformal way.

A second sub-step includes interpreting a query response from anothercomputing device to produce the set of identigens, where the queryresponse is received in response to an identigen query with regards tothe word. For example, the element identification module 302 receivesthe plurality of sets of identigens from the other computing device.

FIG. 10B further illustrates the example steps of the method ofoperation for the processing informal utilization of words, where,having produced the plurality of sets of identigens 808, in a fourthstep the interpretation module 304 identifies an idiolect for the source(e.g., Tom's manner of speaking). For example, the interpretation module304 interprets idiolect information 807 from the knowledge database 700that indicates that Tom frequently uses very informal meanings of words.

Having identified the idiolect for the source, in a fifth step of themethod of operation for the processing informal utilization of words,the interpretation module 304 generates a phrase entigen group 812utilizing identigen rules modified for the source idiolect. Thegenerating of the phrase entigen group 812 includes a series ofsub-steps. A first sub-step includes applying the idiolect information807 to identigen rules 710 associated with identigens of the sets ofidentigens 808 to produce modified identigen rules 711. For example, theinterpretation module 304 produces an allowed identigen pairing rule ofthe modified identigen rules 711 for identigens #10 and #22 and adisallowed identigen pairing rule for identigens #10 and #21 when thesource frequently uses informal meanings of words and identigen #22 isassociated with the informal meaning of the word “plumb” and identigen#21 is not.

A second sub-step includes the interpretation module 304 selectingidentigens #10 for “Fred”, #22 for “plumb”, and #27 for “crazy” toproduce the phrase entigen group 812 when allowed identigen pairings ofthe modified identigen rules 711 includes #10 with #22 and #22 with #27.Having produced the phrase entigen group 812, the interpretation module304 issues the phrase entigen group 812 to the answer resolution module306 for further processing. Alternatively, or in addition to, the answerresolution module 306 facilitates storage of the phrase entigen group812 in the knowledge database 700 as new incremental knowledge.

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 11A-11C are schematic block diagrams of another embodiment of acomputing system illustrating a method for identifying knowledgeassociated with a set of phrases within the computing system. Thecomputing system includes the content ingestion module 300 of FIG. 5E,the element identification module 302 of FIG. 5E, the interpretationmodule 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, anda knowledge database 700. The knowledge database 700 may be implementedutilizing one or more of the memories of FIG. 2.

FIG. 11A illustrates example steps of the method of operation for thefor the identifying knowledge associated with the set of phrases, where,in a first step the content ingestion module 300 obtains a set ofphrases 802 that includes a string of words. The obtaining includesreceiving, looking up, retrieving, and generating. For example, thecontent ingestion module 300 receives the set of phrases 802 thatincludes word strings “heard crash” and “saw police car”.

Having obtained the set of phrases 802, a second step of the method forthe identifying knowledge associated with the set of phrases includesthe content ingestion module 300 parsing the set of phrases 802 togenerate phrase words 804. The parsing includes one or more ofvalidating words of the word strings with valid words of a dictionary703, eliminating unneeded words, replacing undesired words withreplacement words that are desired, and adding additional words that mayprovide enhanced clarity of understanding as is commonly used within agiven language. For example, the content ingestion module 300 obtainsthe dictionary 703 from the knowledge database 700 and validates thewords of the word strings with words of the dictionary 703 to producethe phrase words 804. For instance, the content ingestion module 300compares each word of the word strings “heard crash” and “saw policecar” to words of the dictionary 703 to validate each of the wordsproducing the valid sequence of phrase words 804 that includes “heard”and “crash” of a first word string and “saw”, “police”, and “car” of asecond word string.

In a third step of the method for the identifying knowledge associatedwith the set of phrases, the element identification module 302identifies a set of identigens for each valid word of the phrase words804 to produce a (plurality) of sets of identigens 808. A first set ofidentigens of the plurality of sets of identigens includes one or moredifferent meanings of a first word of the valid sequence of phrasewords.

The identifying the set of identigens includes a series of one or moresub-steps. A first sub-step includes accessing, for each word of thevalid sequence of phrase words 804, the knowledge database 700 torecover the set of identigens. For example, the element identificationmodule 302 receives identigen information 706 from the knowledgedatabase 700 that includes an identigen set #1 for the word “heard”,where the identigen set #1 includes identigens 40, and 41. Theidentigens 40 and 41 represent to unique interpretations of the word“heard”, including “listen” and “aware.” As another example, the elementidentification module 302 receives identigen information 706 from theknowledge database 700 that includes identigens 61, 62, and 63 for theword “saw”, representing meanings of “cut”, “visualized”, and “tool.”

A second sub-step includes interpreting a query response from anothercomputing device to produce the set of identigens, where the queryresponse is received in response to an identigen query with regards tothe word. For example, the element identification module 302 receivesthe plurality of sets of identigens from the other computing device.

FIG. 11B further illustrates the example steps of the method ofoperation for the identifying knowledge associated with the set ofphrases, where, having identified the set of identigens for each validphrase word, a fourth step includes the interpretation module 304generating a set of query entigen groups 713 for the set of phrases. Forexample, the interpretation module 304 accesses the knowledge database700 utilizing the sets of identigens 808 to recover identigen rules 710associated with the identigens of the sets of identigens 808. Havingrecovered the identigen rules 710, the interpretation module 304interprets the identigen rules 710 for the identigens of the sets ofidentigens 808 to identify each query entigen group of the set of queryentigen groups 713. For instance, the interpretation module 304identifies a first query entigen group that includes entigens 40 and 51(e.g., pertaining to listening to a collision) and identifies a secondquery entigen group that includes entigens 62, 71, and 81 (e.g.,pertaining to visualizing a civil force vehicle) when both entigengroups are allowed by the identigen rules 710.

FIG. 11C further illustrates the example steps of the method ofoperation for the identifying knowledge associated with the set ofphrases, where, having produced the set of query entigen groups 713, ina fifth step the answer resolution module 306 accesses the knowledgedatabase 700 utilizing the set of query entigen group 713 to produce aresponse entigen group 720. For example, the answer resolution module306 locates an entigen group within the knowledge database 700 andassociated with the set of query entigen groups 713 representingknowledge associated with dispatching of a police car to a car collisionat 1000 Main St. is located. The answer resolution module 306 interpretsentigen information 714 associated with the located entigen group toproduce the response entigen group 720 to include entigens representingthe car collision at 1000 Main St.

Alternatively, or in addition to, the answer resolution module 306produces a response based on the response entigen group 720. Forexample, the answer resolution module 306 produces a response “a carcollision is at 1000 Main Street.”

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 12A-12C are schematic block diagrams of another embodiment of acomputing system illustrating a method identifying medical conditionswithin the computing system. The computing system includes the contentingestion module 300 of FIG. 5E, the element identification module 302of FIG. 5E, the interpretation module 304 of FIG. 5E, the answerresolution module 306 of FIG. 5E, and a knowledge database 700. Theknowledge database 700 may be implemented utilizing one or more of thememories of FIG. 2.

FIG. 12A illustrates example steps of the method of operation for theidentifying medical conditions, where, in a first step the contentingestion module 300 obtains at least one phrase 802 that includes astring of words associated with a set of medical phrases. The obtainingincludes receiving, looking up, retrieving, and generating. For example,the content ingestion module 300 receives the phrase 802 that includes astring of words “been sick 24 hours” and “with fever and cough.”

Having obtained the phrase 802, a second step includes the contentingestion module 300 parsing the set of medical phrases of the phrase802 to generate phrase words 804. The parsing includes one or more ofvalidating words of the string of words with valid words of a dictionary703, eliminating unneeded words, replacing undesired words withreplacement words that are desired, identifying unknown words, andadding additional words that may provide enhanced clarity ofunderstanding as is commonly used within a given language. For example,the content ingestion module 300 obtains the dictionary 703 from theknowledge database 700 and validates each of the words “been sick 24hours” and “with fever and cough” with the dictionary 703 to producevalidated words of the phrase words 804.

In a third step of the method for the identifying medical conditions,the element identification module 302 identifies a set of identigens foreach valid phrase word for the set of medical phrases of the phrasewords 804 to produce sets of identigens 808. A first set of identigensof the sets of identigens includes one or more different meanings of theword “sick” of the phrase words 804 (e.g., excellent, illness,unpleasant). A third set of identigens of the sets of identigensincludes one or more different meanings of the word “fever” of the validphrase words 804 (e.g., hi body temperature, craze).

The identifying the set of identigens includes a series of one or moresub-steps. A first sub-step includes accessing, for each word of thephrase words 804, the knowledge database 700 to recover the set ofidentigens. For example, the element identification module 302 receivesidentigen information 706 from the knowledge database 700 that includesthe first identigen set for the word “sick” where the first identigenset includes identigens #90, 91, and 92.

A second sub-step includes interpreting a query response from anothercomputing device to produce the set of identigens, where the queryresponse is received in response to an identigen query with regards tothe word. For example, the element identification module 302 receivesthe sets of identigens from the other computing device.

FIG. 12B further illustrates the example steps of the method ofoperation for the identifying medical conditions, where, having obtainedthe set of identigens 808, a fourth step includes the interpretationmodule 304 generating a set of query entigen group 713 based on the setsof identigens 808. For example, the interpretation module 304 accessesthe knowledge database 700 utilizing the identigens of the sets ofidentigens 808 to recover medical identigen rules 709 associated withidentigens of a medical domain.

The interpretation module 304 interprets the medical identigen rules 709with regards to the sets of identigens 808 to identify validpermutations of identigens to produce the set of query entigen group713. For instance, the interpretation module 304 interprets the medicalidentigen rules 709 to produce a first query entigen group of the set ofquery entigen groups 713 that includes identigen #91 associated with ameaning of “illness” and identigen #95 associated with the meaning of “aday.” As another instance, the interpretation module 304 interprets themedical identigen rules 709 to produce a second query entigen group ofthe set of query entigen groups 713 that includes identigen #75associated with a meaning of “high body temperature” and identigen #66associated with “sound of coughing.”

FIG. 12C further illustrates the example steps of the method ofoperation for the identifying medical conditions, where having obtainedthe set of query entigen groups 713, in a fifth step the answerresolution module 306 accesses the knowledge database 700 utilizing theset of query entigen group 713 to produce a medical response entigengroup 721. For example, the answer resolution module 306 accesses aportion of the knowledge database 700 to locate an entigen groupassociated with each of the query entigen groups of the set of queryentigen group 713. The answer resolution module 306 interprets medicalentigen information 717 associated with the located entigen group toproduce the medical response entigen group 721. For example, the answerresolution module 306 locates entigens associated with illnesses withsymptoms of coughing and fever to include cold, influenza, pneumonia,pandemic virus, and bronchitis.

Alternatively, or in addition to, the answer resolution module 306generates a response based on the medical response entigen group 721.For example, the answer resolution module 306 issues a response toinclude “illnesses include a cold, influenza, pneumonia, pandemic virus,and bronchitis.

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 13A-13B are schematic block diagrams of another embodiment of acomputing system illustrating a method for identifying true meaning of asarcastic phrase within the computing system. The computing systemincludes the content ingestion module 300 of FIG. 5E, the elementidentification module 302 of FIG. 5E, the interpretation module 304 ofFIG. 5E, the answer resolution module 306 of FIG. 5E, and a knowledgedatabase 700. The knowledge database 700 may be implemented utilizingone or more of the memories of FIG. 2.

FIG. 13A illustrates example steps of the method of operation for theidentifying true meaning of the sarcastic phrase, where, in a first stepthe computing system obtains a phrase 802 that includes a set of phrasesfrom a subject (e.g., John). For example, the content ingestion module300 obtains the phrase 802 that includes John's words “truck broken” and“I love my truck.” In a second step of the example method of theidentifying true meaning of the sarcastic phrase, the content ingestionmodule 300 generates valid phrase words 804 from the set of phrases ofthe phrase 802. For example, the content ingestion module 300 validatesthe words of the phrase utilizing words from a dictionary 703 of theknowledge database 700 to produce the phrase words 804 to include“truck”, “broken” and “I”, “love”, and “truck.”

In a third step of the example method of the identifying true meaning ofthe sarcastic phrase, the element identification module 302, for eachword of the phrase words 804, identifies a set of identigens to producea plurality of sets of identigens 808. For example, the elementidentification module 302 utilizes the phrase words 804 to access theknowledge database 700 to obtain identigen information 706 thatincludes, for each word, the set of identigens to produce the sets ofidentigens 808. For instance, the word “truck” results in a first set ofidentigens including identigen #35 for “transport” and identigen #45 for“broken.” In another instance, the word “love” results in a fourth setof identigens including identigen number #15 for “to adore”, identigen#16 for “feeling”, and identigen #17 for a sarcastic meaning associatedwith “hate.”

FIG. 13B further illustrates the example steps of the method ofoperation for the identifying true meaning of the sarcastic phrase,where, having obtained the sets of identigens 808, in a fourth step theinterpretation module 304 identifies an idiolect for the subject. Forexample, the interpretation module 304 obtains idiolect information 807with regards to John from the knowledge database 700, where the idiolectinformation 807 indicates that John frequently uses words sarcastically.

Having obtained the idiolect information 807, a fifth step of theexample method of operation for the identifying true meaning of thesarcastic phrase includes the interpretation module 304 generating aphrase entigen group 812 utilizing identigen rules modified for thesarcastic source idiolect. The generating of the phrase entigen group812 includes a series of sub-steps. A first sub-step includes modifyingidentigen rules 710 to produce modified identigen rules 711, where themodified identigen rules 711 accommodate sarcastic interpretations ofwords by pairing identigens associated with sarcastic meanings of words.For example, the interpretation module 304 generates the modifiedidentigen rules 711 to include an allowed pairing of identigens #25 and#17 and another allowed pairing of identigens #17 and #36 when thesubject frequently uses words sarcastically and the identigen #17 isassociated with a sarcastic meaning (e.g., “hate” meaning for word“love”).

A second sub-step includes utilizing the modified identigen rules 711 tointerpret the sets of identigens 808 to produce the phrase entigen group812. For example, the interpretation module 304 generates the phraseentigen group 812 to include entigen #45 for a meaning of “disrepair”,entigen #36 for a meaning of “vehicle”, an entigen for “John”, andentigen #17 for a meaning of “hate.”

Alternatively, or in addition to, the answer resolution module 306 formsa response from the phrase entigen group 812. For example, the answerresolution module 306 generates the response to include “John hates hisvehicle (e.g., truck) in disrepair.” Further alternatively, or inaddition to, the answer resolution module 306 stores the phrase entigengroup 812 in the knowledge database 700 as incremental new knowledge torecord the observation that John hates his truck that is in disrepair.

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIGS. 14A-14D are schematic block diagrams of another embodiment of acomputing system illustrating a method for processing a query within thecomputing system. The computing system includes the content ingestionmodule 300 of FIG. 5E, the element identification module 302 of FIG. 5E,the interpretation module 304 of FIG. 5E, the answer resolution module306 of FIG. 5E, and a knowledge database 700. The knowledge database 700may be implemented utilizing one or more of the memories of FIG. 2.

FIG. 14A illustrates example steps of the method of operation for theprocessing the query, where, in a first step the content ingestionmodule 300 obtains a query 702 that includes a string of words. Theobtaining includes receiving, looking up, retrieving, and generating.For example, the content ingestion module 300 receives the query 702that includes a string of words “bat animal colors?”.

Having obtained the query 702, a second step of the method for theprocessing of the query includes the content ingestion module 300parsing the query 702 to generate query words 704. The parsing includesone or more of validating words of the string of words with valid wordsof a dictionary 703, eliminating unneeded words, replacing undesiredwords with replacement words that are desired, and adding additionalwords that may provide enhanced clarity of understanding as is commonlyused within a given language. For example, the content ingestion module300 obtains the dictionary 703 from the knowledge database 700 andvalidates the words of the string of words with words of the dictionary703 to produce the query words 704. For instance, the content ingestionmodule 300 compares each word of the string of words “bat animalcolors?” to words of the dictionary 703 to validate each of the wordsproducing the valid sequence of query words 704 that includes “bat,animal, colors.”

In a third step of the method for the determining meaning of the phrase,the element identification module 302 identifies a set of identigens foreach valid word of the query words 704 to produce a (plurality) of setsof identigens 708. A first set of identigens of the plurality of sets ofidentigens includes one or more different meanings of a first word ofthe valid sequence of query words 704.

The identifying the set of identigens includes a series of sub-steps. Afirst sub-step includes accessing, for each word of the valid sequenceof query words 704, the knowledge database 700 to recover the set ofidentigens. For example, the element identification module 302 receivesidentigen information 706 from the knowledge database 700 that includesan identigen set #1 for the word “bat”, where the identigen set #1includes identigen #7 for a meaning of “baseball bat”, identigen #8 forthe meaning of “animal bat”, and identigen #9 for a meaning of “to hit.”As another example, the element identification module 302 receivesidentigen information 706 from the knowledge database 700 that includesan identigen sets #3 for the word “colors”, including identigens #1-#3associated with meanings of “appearance”, “skin color”, and “changecolor” respectively.

A second sub-step includes interpreting a query response from anothercomputing device to produce the set of identigens, where the queryresponse is received in response to an identigen query with regards tothe word. For example, the element identification module 302 receivesthe plurality of sets of identigens from the other computing device.

FIG. 14B further illustrates the example steps of the method ofoperation for the processing of the query, where, having obtained theplurality of sets of identigens 708, in a fourth step the interpretationmodule 304 applies identigen rules 710 to the sets of identigens 708 togenerate a query entigen group 712. For example, the interpretationmodule 304 interprets identigen rules 710 from the knowledge database700 with regards to the identigens of the set of identigens 708. Forinstance, the interpretation module 304 interprets the identigen rules710 to determine that the identigen #8 is allowed to pair with identigen#0 and identigen number #8 is allowed to pair with identigen #1associated with color appearance, the object of the query.

FIG. 14C further illustrates the example steps of the method ofoperation for the processing of the query, where, having obtained thequery entigen group 712, in a fifth step the answer resolution module306 accesses the knowledge database 700 using the query entigen group712 to generate a response entigen group 920. For example, the answerresolution module 306 interprets entigen information 714 from theknowledge database to identify color characteristic entigens associatedwith the color appearance entigen #1 including gray, white, brown, andblack to include in the response entigen group 920 along with entigen #0for “living organism”, entigen #8 for “animal bat”, and entigen #1 for“color appearance”.

Having produced the response entigen group 920, in a 6 step of theexample method of operation for the processing of the query, the answerresolution module 306 determines that the response entigen group 920 isunfavorable. The determining of the unfavorable response entigen group920 includes indicating that the response entigen group 920 isunfavorable when one or more indicators have been detected. Theindicators includes detecting that a timeframe since a last update ofcolor characteristic entigens is greater than a time threshold,determining that a number of characteristic entigens is less than acharacteristic threshold level, and detecting that a date of knowledgeassociated with bat colors is older than an age threshold level.

When the response entigen group 920 is unfavorable, a seventh step ofthe example method of the processing of the query includes the answerresolution module 306 obtaining further related knowledge. For example,the answer resolution module 306 issues a content request 900 to one ormore content sources for content associated with color characteristicsof animal bats to produce content 902 4 ingestion by the computingsystem. In an instance, the content ingestion module 300 receives 10,000more documents associated with bat animals as content 902, and generatescontent words 904 that includes “bat animal colors include orange andred.”

The element identification module 302 generates content sets ofidentigens 908 based on the content words 904 and the interpretationmodule 304 generates a content entigen groups 912 based on the contentsets of identigens 908, where the content entigen group 912 includes thecolor characteristic entigens of orange and red for the color appearanceof the bat animal.

FIG. 14D further illustrates the example steps of the method ofoperation for the processing of the query, where, having obtained thecontent entigen group 912, in and eighth step the answer resolutionmodule 306 generates an updated response entigen group 922 utilizing thefurther related knowledge. For example, the answer resolution module 306combines the response entigen group 920 and the content entigen group912 to produce the updated response entigen group 922. For instance, theanswer resolution module 306 generates the updated response entigengroup to include color characteristic entigens of grey, white, brown,black, red, and orange to describe the color appearance of the batanimal.

Alternatively, or in addition to, the answer resolution module 306facilitates generation of a response based on the updated responseentigen group 922. For example, the answer resolution module 306generates the response to include “bat animal colors include grey,white, brown, black, red, and orange.” Further alternatively, or inaddition to, the answer resolution module 306 facilitates storage of thefurther related knowledge in the knowledge database 700. For example,the answer resolution module 306 issues entigen information 714 to theknowledge database 700, where the entigen information 714 includes thecharacteristic entigens for orange and red to be associated with thecolor appearance entigen within the knowledge database 700. As anotherexample, the answer resolution module 306 issues other entigeninformation 714 to the knowledge database 700, where the entigeninformation 714 excludes a previously stored appearance colorcharacteristic entigen when the further related knowledge includesdisconfirming information. For instance, removing the white colorcharacteristic when the content 902 conclusively indicates that whitebats are a myth and do not occur in nature.

The method described with reference to the preceding figures and inconjunction with the various modules can alternatively be performed byother modules of the computing system 10 of FIG. 1 or by other devices.In addition, at least one memory section (e.g., a non-transitorycomputer readable memory, a non-transitory computer readable storagemedium, a non-transitory computer readable memory organized into a firstmemory element, a second memory element, a third memory element, afourth element section, a fifth memory element etc.) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., one or more servers, oneor more user devices) of the computing system 10, cause the one or morecomputing devices to perform any or all of the method steps describedabove.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a computing device, themethod comprises: determining a set of identigens for each query word ofa query to produce a plurality of sets of identigens, wherein a set ofidentigens of the plurality of sets of identigens represents one or moredifferent meanings of a query word of the query, wherein each identigenof the set of identigens includes a meaning identifier, an instanceidentifier, and a time reference, wherein each meaning identifierassociated with a particular set of identigens represents a differentmeaning of one or more different meanings of a corresponding query wordof the query, wherein each time reference provides time information whena corresponding different meaning of the one or more different meaningsis valid; interpreting, based on identigen pairing rules of a knowledgedatabase, pairs of sequentially adjacent identigens of adjacent sets ofidentigens of the plurality of sets of identigens to determine a firstmost likely meaning interpretation of the query and produce a firstquery entigen group of a plurality of query entigen groups, wherein theplurality of query entigen groups are associated with a plurality ofmost likely meaning interpretations of the query, wherein the pluralityof most likely meaning interpretations of the query includes the firstmost likely meaning interpretation of the query, wherein the first queryentigen group represents the first most likely meaning interpretation ofthe query, wherein each entigen of the first query entigen groupcorresponds to a selected identigen of one of the plurality of sets ofidentigens having a selected meaning identifier of the one or moredifferent meanings of a corresponding query word of the query words thatrepresents a first most likely meaning interpretation of thecorresponding query word, wherein each selected identigen correspondingto the first query entigen group favorably pairs with at least onecorresponding sequentially adjacent identigen of an adjacent set ofidentigens in accordance with the identigen pairing rules, wherein eachentigen of the first query entigen group represents a single conceivableand perceivable thing in space and time that is independent of languageand corresponds to a time reference of a corresponding selectedidentigen associated with the first query entigen group, wherein theknowledge database includes a plurality of records that link entigenshaving a connected meaning; identifying an inconsistency between atleast two query entigen groups of the plurality of query entigen groups;obtaining an inconsistency clarification based on the inconsistencybetween the at least two query entigen groups of the plurality of queryentigen groups; and selecting one query entigen group of the pluralityof query entigen groups based on the inconsistency clarification toproduce a final query entigen group.
 2. The method of claim 1 furthercomprises: interpreting, based on the identigen pairing rules of theknowledge database, the pairs of sequentially adjacent identigens of theadjacent sets of identigens of the plurality of sets of identigens todetermine a second most likely meaning interpretation of the query andproduce a second query entigen group of the plurality of query entigengroups, wherein the plurality of most likely meaning interpretations ofthe query includes the second most likely meaning interpretation of thequery, wherein the second query entigen group represents the second mostlikely meaning interpretation of the query, wherein each entigen of thesecond query entigen group corresponds to a selected identigen of one ofthe plurality of sets of identigens having a selected meaning identifierof the one or more different meanings of a corresponding query word ofthe query words that represents a second most likely meaninginterpretation of the corresponding query word, wherein each selectedidentigen corresponding to the second query entigen group favorablypairs with at least one corresponding sequentially adjacent identigen ofan adjacent set of identigens in accordance with the identigen pairingrules, wherein each entigen of the second query entigen group representsa single conceivable and perceivable thing in space and time that isindependent of language and corresponds to a time reference of acorresponding selected identigen associated with the second queryentigen group.
 3. The method of claim 1 further comprises: accessing theknowledge database utilizing the final query entigen group to produce aresponse entigen group; and generating a response to the query utilizingthe response entigen group, wherein the response includes at least oneresponse word.
 4. The method of claim 1, wherein the identifying theinconsistency between at least two query entigen groups of the pluralityof query entigen groups comprises one or more of: identifying a firstquery entigen of the first query entigen group that is different than afirst query entigen of a second query entigen group; and determiningthat a number of query entigens of the first query entigen group isdifferent than a number of query entigens of the second query entigengroup.
 5. The method of claim 1, wherein the obtaining the inconsistencyclarification based on the inconsistency between the at least two queryentigen groups of the plurality of query entigen groups comprises atleast one of: accessing the knowledge database utilizing the first queryentigen group to produce a clarification entigen group as theinconsistency clarification; and interpreting a clarification responseto a clarification request to produce the clarification entigen group asthe inconsistency clarification, wherein the clarification request isbased on the inconsistency between the at least two query entigengroups.
 6. The method of claim 1, wherein the selecting the one queryentigen group of the plurality of query entigen groups based on theinconsistency clarification to produce the final query entigen groupcomprises: obtaining a clarification entigen group based on theinconsistency clarification; and selecting the one query entigen groupof the plurality of query entigen groups that is most consistent withthe clarification entigen group to produce the final query entigengroup.
 7. A computing device of a computing system, the computing devicecomprises: an interface; a local memory; and a processing moduleoperably coupled to the interface and the local memory, wherein theprocessing module performs functions to: determine a set of identigensfor each query word of a query to produce a plurality of sets ofidentigens, wherein a set of identigens of the plurality of sets ofidentigens represents one or more different meanings of a query word ofthe query, wherein each identigen of the set of identigens includes ameaning identifier, an instance identifier, and a time reference,wherein each meaning identifier associated with a particular set ofidentigens represents a different meaning of one or more differentmeanings of a corresponding query word of the query, wherein each timereference provides time information when a corresponding differentmeaning of the one or more different meanings is valid; interpret, basedon identigen pairing rules of a knowledge database, pairs ofsequentially adjacent identigens of adjacent sets of identigens of theplurality of sets of identigens to determine a first most likely meaninginterpretation of the query and produce a first query entigen group of aplurality of query entigen groups, wherein the plurality of queryentigen groups are associated with a plurality of most likely meaninginterpretations of the query, wherein the plurality of most likelymeaning interpretations of the query includes the first most likelymeaning interpretation of the query, wherein the first query entigengroup represents the first most likely meaning interpretation of thequery, wherein each entigen of the first query entigen group correspondsto a selected identigen of one of the plurality of sets of identigenshaving a selected meaning identifier of the one or more differentmeanings of a corresponding query word of the query words thatrepresents a first most likely meaning interpretation of thecorresponding query word, wherein each selected identigen correspondingto the first query entigen group favorably pairs with at least onecorresponding sequentially adjacent identigen of an adjacent set ofidentigens in accordance with the identigen pairing rules, wherein eachentigen of the first query entigen group represents a single conceivableand perceivable thing in space and time that is independent of languageand corresponds to a time reference of a corresponding selectedidentigen associated with the first query entigen group, wherein theknowledge database includes a plurality of records that link entigenshaving a connected meaning; identify an inconsistency between at leasttwo query entigen groups of the plurality of query entigen groups;obtain an inconsistency clarification based on the inconsistency betweenthe at least two query entigen groups of the plurality of query entigengroups; and select one query entigen group of the plurality of queryentigen groups based on the inconsistency clarification to produce afinal query entigen group.
 8. The computing device of claim 7, whereinthe processing module further performs functions to: interpret, based onthe identigen pairing rules of the knowledge database, the pairs ofsequentially adjacent identigens of the adjacent sets of identigens ofthe plurality of sets of identigens to determine a second most likelymeaning interpretation of the query and produce a second query entigengroup of the plurality of query entigen groups, wherein the plurality ofmost likely meaning interpretations of the query includes the secondmost likely meaning interpretation of the query, wherein the secondquery entigen group represents the second most likely meaninginterpretation of the query, wherein each entigen of the second queryentigen group corresponds to a selected identigen of one of theplurality of sets of identigens having a selected meaning identifier ofthe one or more different meanings of a corresponding query word of thequery words that represents a second most likely meaning interpretationof the corresponding query word, wherein each selected identigencorresponding to the second query entigen group favorably pairs with atleast one corresponding sequentially adjacent identigen of an adjacentset of identigens in accordance with the identigen pairing rules,wherein each entigen of the second query entigen group represents asingle conceivable and perceivable thing in space and time that isindependent of language and corresponds to a time reference of acorresponding selected identigen associated with the second queryentigen group.
 9. The computing device of claim 7, wherein theprocessing module further performs functions to: access, via theinterface, the knowledge database utilizing the final query entigengroup to produce a response entigen group; and generate a response tothe query utilizing the response entigen group, wherein the responseincludes at least one response word.
 10. The computing device of claim7, wherein the processing module performs functions to identify theinconsistency between at least two query entigen groups of the pluralityof query entigen groups by one or more of: identifying a first queryentigen of the first query entigen group that is different than a firstquery entigen of a second query entigen group; and determining that anumber of query entigens of the first query entigen group is differentthan a number of query entigens of the second query entigen group. 11.The computing device of claim 7, wherein the processing module performsfunctions to obtain the inconsistency clarification based on theinconsistency between the at least two query entigen groups of theplurality of query entigen groups by at least one of: accessing, via theinterface, the knowledge database utilizing the first query entigengroup to produce a clarification entigen group as the inconsistencyclarification; and interpreting a clarification response to aclarification request to produce the clarification entigen group as theinconsistency clarification, wherein the clarification request is basedon the inconsistency between the at least two query entigen groups. 12.The computing device of claim 7, wherein the processing module performsfunctions to select the one query entigen group of the plurality ofquery entigen groups based on the inconsistency clarification to producethe final query entigen group by: obtaining a clarification entigengroup based on the inconsistency clarification; and selecting the onequery entigen group of the plurality of query entigen groups that ismost consistent with the clarification entigen group to produce thefinal query entigen group.
 13. A computer readable memory comprises: afirst memory element that stores operational instructions that, whenexecuted by a processing module, causes the processing module to:determine a set of identigens for each query word of a query to producea plurality of sets of identigens, wherein a set of identigens of theplurality of sets of identigens represents one or more differentmeanings of a query word of the query, wherein each identigen of the setof identigens includes a meaning identifier, an instance identifier, anda time reference, wherein each meaning identifier associated with aparticular set of identigens represents a different meaning of one ormore different meanings of a corresponding query word of the query,wherein each time reference provides time information when acorresponding different meaning of the one or more different meanings isvalid; a second memory element that stores operational instructionsthat, when executed by the processing module, causes the processingmodule to: interpret, based on identigen pairing rules of a knowledgedatabase, pairs of sequentially adjacent identigens of adjacent sets ofidentigens of the plurality of sets of identigens to determine a firstmost likely meaning interpretation of the query and produce a firstquery entigen group of a plurality of query entigen groups, wherein theplurality of query entigen groups are associated with a plurality ofmost likely meaning interpretations of the query, wherein the pluralityof most likely meaning interpretations of the query includes the firstmost likely meaning interpretation of the query, wherein the first queryentigen group represents the first most likely meaning interpretation ofthe query, wherein each entigen of the first query entigen groupcorresponds to a selected identigen of one of the plurality of sets ofidentigens having a selected meaning identifier of the one or moredifferent meanings of a corresponding query word of the query words thatrepresents a first most likely meaning interpretation of thecorresponding query word, wherein each selected identigen correspondingto the first query entigen group favorably pairs with at least onecorresponding sequentially adjacent identigen of an adjacent set ofidentigens in accordance with the identigen pairing rules, wherein eachentigen of the first query entigen group represents a single conceivableand perceivable thing in space and time that is independent of languageand corresponds to a time reference of a corresponding selectedidentigen associated with the first query entigen group, wherein theknowledge database includes a plurality of records that link entigenshaving a connected meaning; a third memory element that storesoperational instructions that, when executed by the processing module,causes the processing module to: identify an inconsistency between atleast two query entigen groups of the plurality of query entigen groups;a fourth memory element that stores operational instructions that, whenexecuted by the processing module, causes the processing module to:obtain an inconsistency clarification based on the inconsistency betweenthe at least two query entigen groups of the plurality of query entigengroups; and a fifth memory element that stores operational instructionsthat, when executed by the processing module, causes the processingmodule to: select one query entigen group of the plurality of queryentigen groups based on the inconsistency clarification to produce afinal query entigen group.
 14. The computer readable memory of claim 13further comprises: a sixth memory element stores operationalinstructions that, when executed by the processing module, causes theprocessing module to: interpret, based on the identigen pairing rules ofthe knowledge database, the pairs of sequentially adjacent identigens ofthe adjacent sets of identigens of the plurality of sets of identigensto determine a second most likely meaning interpretation of the queryand produce a second query entigen group of the plurality of queryentigen groups, wherein the plurality of most likely meaninginterpretations of the query includes the second most likely meaninginterpretation of the query, wherein the second query entigen grouprepresents the second most likely meaning interpretation of the query,wherein each entigen of the second query entigen group corresponds to aselected identigen of one of the plurality of sets of identigens havinga selected meaning identifier of the one or more different meanings of acorresponding query word of the query words that represents a secondmost likely meaning interpretation of the corresponding query word,wherein each selected identigen corresponding to the second queryentigen group favorably pairs with at least one correspondingsequentially adjacent identigen of an adjacent set of identigens inaccordance with the identigen pairing rules, wherein each entigen of thesecond query entigen group represents a single conceivable andperceivable thing in space and time that is independent of language andcorresponds to a time reference of a corresponding selected identigenassociated with the second query entigen group.
 15. The computerreadable memory of claim 13 further comprises: a seventh memory elementstores operational instructions that, when executed by the processingmodule, causes the processing module to: access the knowledge databaseutilizing the final query entigen group to produce a response entigengroup; and generate a response to the query utilizing the responseentigen group, wherein the response includes at least one response word.16. The computer readable memory of claim 13, wherein the processingmodule functions to execute the operational instructions stored by thethird memory element to cause the processing module to identify theinconsistency between at least two query entigen groups of the pluralityof query entigen groups by one or more of: identifying a first queryentigen of the first query entigen group that is different than a firstquery entigen of a second query entigen group; and determining that anumber of query entigens of the first query entigen group is differentthan a number of query entigens of the second query entigen group. 17.The computer readable memory of claim 13, wherein the processing modulefunctions to execute the operational instructions stored by the fourthmemory element to cause the processing module to obtain theinconsistency clarification based on the inconsistency between the atleast two query entigen groups of the plurality of query entigen groupsby at least one of: accessing the knowledge database utilizing the firstquery entigen group to produce a clarification entigen group as theinconsistency clarification; and interpreting a clarification responseto a clarification request to produce the clarification entigen group asthe inconsistency clarification, wherein the clarification request isbased on the inconsistency between the at least two query entigengroups.
 18. The computer readable memory of claim 13, wherein theprocessing module functions to execute the operational instructionsstored by the fifth memory element to cause the processing module toselect the one query entigen group of the plurality of query entigengroups based on the inconsistency clarification to produce the finalquery entigen group by: obtaining a clarification entigen group based onthe inconsistency clarification; and selecting the one query entigengroup of the plurality of query entigen groups that is most consistentwith the clarification entigen group to produce the final query entigengroup.